{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一期 Pandas基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.将下面的字典创建为DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"grammer\":[\"Python\",\"C\",\"Java\",\"GO\",\"R\",\"SQL\",\"PHP\",\"Python\"],\n",
    "       \"score\":[1,2,np.nan,4,5,6,7,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  score\n",
       "0  Python    1.0\n",
       "1       C    2.0\n",
       "2    Java    NaN\n",
       "3      GO    4.0\n",
       "4       R    5.0\n",
       "5     SQL    6.0\n",
       "6     PHP    7.0\n",
       "7  Python   10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.提取含有字符串\"Python\"的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  score\n",
       "0  Python    1.0\n",
       "7  Python   10.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "# df.where(df.grammer=='Python').dropna()\n",
    "\n",
    "# 方法二\n",
    "df[df.grammer.str.contains('Python')]\n",
    "\n",
    "# # 方法三\n",
    "# df[df.grammer == 'Python']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.输出df的所有列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['grammer', 'score'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.修改第二列列名为'popularity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         NaN\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一，score --> popularity\n",
    "df.columns = ['grammer', 'popularity']  # 这种方法不支持修改单个，只能修改所有的\n",
    "df\n",
    "\n",
    "# # 方法二，popularity --> score\n",
    "# df.rename(columns={'popularity': 'score'}, inplace=True)\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.统计grammer列中每种编程语言出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    2\n",
       "Java      1\n",
       "PHP       1\n",
       "R         1\n",
       "SQL       1\n",
       "GO        1\n",
       "C         1\n",
       "Name: grammer, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 方法一\n",
    "# df.groupby(by=['grammer']).grammer.count()\n",
    "\n",
    "# 方法二\n",
    "df.grammer.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.将空值用上下值的平均值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         5.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df.mean(), inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.提取popularity列中值大于3的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "2    Java         5.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 方法一\n",
    "# df.where(df.popularity>3).dropna()\n",
    "\n",
    "# # 方法二\n",
    "# df[df.popularity > 3]\n",
    "\n",
    "# 方法三\n",
    "df.query('popularity > 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.按照grammer列进行去除重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(['grammer'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.计算popularity列平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.285714285714286"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.popularity.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.将grammer列转换为list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python', 'C', 'Java', 'GO', 'R', 'SQL', 'PHP']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "list(df.grammer)\n",
    "\n",
    "# # 方法二\n",
    "# df.grammer.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.将DataFrame保存为EXCEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel('test.xlsx')\n",
    "df.to_excel('test.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.查看数据行列数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.提取popularity列值大于3小于7的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "2    Java         5.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "# df[(df.popularity > 3) & (df.popularity < 7)]\n",
    "\n",
    "# 方法二\n",
    "df.query(\"popularity > 3 & popularity < 7\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14.交换两列位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         5.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "df = pd.DataFrame(data={df.columns[1]: list(df.popularity), df.columns[0]: list(df.grammer)})\n",
    "df\n",
    "\n",
    "# # 方法二\n",
    "# col = list(df)\n",
    "# col.insert(0, col.pop(col.index('grammer')))\n",
    "# df = df.loc[:, col]\n",
    "# df\n",
    "\n",
    "# # 方法三\n",
    "# df = df.loc[:, ['popularity', 'grammer']]\n",
    "# df\n",
    "\n",
    "# # 方法四\n",
    "# cols = df.columns[[1, 0]]\n",
    "# df = df[cols]\n",
    "# df\n",
    "\n",
    "# # 方法五\n",
    "# temp = df.grammer\n",
    "# df.drop(labels=['grammer'], axis=1, inplace=True)\n",
    "# df.insert(0, 'grammer', temp)\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15.提取popularity列最大值所在行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[['grammer', 'popularity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.popularity.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16.查看最后5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17.删除最后一行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 方法一\n",
    "# df = df[:len(df) - 1]\n",
    "# df\n",
    "\n",
    "# # 方法二\n",
    "# df.drop([len(df) - 1], inplace=True)\n",
    "# df\n",
    "\n",
    "# 错误\n",
    "# del df.iloc[len(df)-1]\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18.添加一行数据['Perl',6.6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Perl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         5.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python\n",
       "8         6.6    Perl"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append({'popularity': 6.6, 'grammer': 'Perl'}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19.对数据按照\"popularity\"列值的大小进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "3         4.0      GO\n",
       "2         5.0    Java\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['popularity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20.统计grammer列每个字符串的长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    (Python, 6)\n",
       "1         (C, 1)\n",
       "2      (Java, 4)\n",
       "3        (GO, 2)\n",
       "4         (R, 1)\n",
       "5       (SQL, 3)\n",
       "6       (PHP, 3)\n",
       "7    (Python, 6)\n",
       "Name: grammer, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "# le = {item: len(item) for item in df.grammer.values}\n",
    "# le\n",
    "\n",
    "# 方法二\n",
    "df.grammer.apply(lambda x: (x, len(x)))\n",
    "\n",
    "# # 方法三\n",
    "# df.grammer.str.len()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二期 Pandas数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21.读取本地EXCEL数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-40k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>13k-20k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>10k-20k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime education   salary\n",
       "0 2020-03-16 11:30:18        本科  20k-35k\n",
       "1 2020-03-16 10:58:48        本科  20k-40k\n",
       "2 2020-03-16 10:46:39        不限  20k-35k\n",
       "3 2020-03-16 10:45:44        本科  13k-20k\n",
       "4 2020-03-16 10:20:41        本科  10k-20k"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_excel('pandas120.xlsx')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 22.查看df数据前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-40k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>13k-20k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>10k-20k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime education   salary\n",
       "0 2020-03-16 11:30:18        本科  20k-35k\n",
       "1 2020-03-16 10:58:48        本科  20k-40k\n",
       "2 2020-03-16 10:46:39        不限  20k-35k\n",
       "3 2020-03-16 10:45:44        本科  13k-20k\n",
       "4 2020-03-16 10:20:41        本科  10k-20k"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 23.将salary列数据转换为最大值与最小值的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "for i in range(len(df2)):\n",
    "    salary_str = df2.iloc[i, 2]\n",
    "    salary_list = re.findall(r'\\d+\\.?\\d*', salary_str)\n",
    "    salary_mean = (int(salary_list[0]) + int(salary_list[1])) / 2 * 1000\n",
    "    df2.iloc[i, 2] = salary_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime education salary\n",
       "0 2020-03-16 11:30:18        本科  27500\n",
       "1 2020-03-16 10:58:48        本科  30000\n",
       "2 2020-03-16 10:46:39        不限  27500\n",
       "3 2020-03-16 10:45:44        本科  16500\n",
       "4 2020-03-16 10:20:41        本科  15000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24.将数据根据学历进行分组并计算最高薪资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "不限    30000.0\n",
       "大专    15000.0\n",
       "本科    45000.0\n",
       "硕士    37500.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(by='education').salary.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 25.将createTime列时间转换为月-日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df2)):\n",
    "    df2.iloc[i, 0] = df2.iloc[i, 0].to_pydatetime().strftime(\"%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary\n",
       "0      03-16        本科  27500\n",
       "1      03-16        本科  30000\n",
       "2      03-16        不限  27500\n",
       "3      03-16        本科  16500\n",
       "4      03-16        本科  15000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 26.查看索引、数据类型和内存信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135 entries, 0 to 134\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   createTime  135 non-null    object\n",
      " 1   education   135 non-null    object\n",
      " 2   salary      135 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 27.查看数值型列的汇总统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>133</td>\n",
       "      <td>119</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       createTime education   salary\n",
       "count         135       135    135.0\n",
       "unique          3         4     36.0\n",
       "top         03-16        本科  30000.0\n",
       "freq          133       119     19.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 28.新增一列根据salary将数据分为三组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary rank\n",
       "0      03-16        本科  27500    高\n",
       "1      03-16        本科  30000    高\n",
       "2      03-16        不限  27500    高\n",
       "3      03-16        本科  16500    中\n",
       "4      03-16        本科  15000    中"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_cut = [0, 5000, 20000, 50000]\n",
    "g_name = ['低', '中', '高']\n",
    "\n",
    "df2['rank'] = pd.cut(df2.salary, bins=g_cut, labels=g_name)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 29.按照salary列对数据降序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>45000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>40000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary rank\n",
       "53       03-16        本科  45000    高\n",
       "37       03-16        本科  40000    高\n",
       "101      03-16        本科  37500    高\n",
       "16       03-16        本科  37500    高\n",
       "131      03-16        硕士  37500    高"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by=['salary'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 30.取出第33行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    03-16\n",
       "education        硕士\n",
       "salary        22500\n",
       "rank              高\n",
       "Name: 32, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 方法一\n",
    "# df2[32:33]\n",
    "\n",
    "# # 方法二\n",
    "# df2.iloc[32]\n",
    "\n",
    "# 方法三\n",
    "df2.loc[32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31.计算salary列的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17500.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 方法一\n",
    "# df2.salary.median()\n",
    "\n",
    "# 方法二\n",
    "np.median(df2.salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32.绘制薪资水平频率分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=100)\n",
    "df2.salary.plot(kind='hist')\n",
    "plt.xticks(color='w')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 33.绘制薪资水平密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-40000., -20000.,      0.,  20000.,  40000.,  60000.,  80000.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.salary.plot(kind='kde')\n",
    "plt.xticks(color='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 34.删除最后一列categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df2 = df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary\n",
       "0        03-16        本科  27500\n",
       "1        03-16        本科  30000\n",
       "2        03-16        不限  27500\n",
       "3        03-16        本科  16500\n",
       "4        03-16        本科  15000\n",
       "..         ...       ...    ...\n",
       "130      03-16        本科  14000\n",
       "131      03-16        硕士  37500\n",
       "132      03-16        本科  30000\n",
       "133      03-16        本科  19000\n",
       "134      03-16        本科  30000\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法一\n",
    "del df2['rank']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 方法二\n",
    "# test_df2.iloc[:, :3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 35.将df的第一列与第二列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['test'] = df2.createTime + df2['education']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>03-16本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>03-16本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>03-16本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary     test\n",
       "0      03-16        本科  27500  03-16本科\n",
       "1      03-16        本科  30000  03-16本科\n",
       "2      03-16        不限  27500  03-16不限\n",
       "3      03-16        本科  16500  03-16本科\n",
       "4      03-16        本科  15000  03-16本科"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 36.将education列与salary列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16不限</td>\n",
       "      <td>不限27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科15000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary     test      test2\n",
       "0      03-16        本科  27500  03-16本科  本科27500.0\n",
       "1      03-16        本科  30000  03-16本科  本科30000.0\n",
       "2      03-16        不限  27500  03-16不限  不限27500.0\n",
       "3      03-16        本科  16500  03-16本科  本科16500.0\n",
       "4      03-16        本科  15000  03-16本科  本科15000.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['test2'] = df2.education + df2['salary'].map(str)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 37.计算salary最大值与最小值之差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary    41500.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['salary']].apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 38.将第一行与最后一行拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科19000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test2\n",
       "0        03-16        本科  27500  03-16本科  本科27500.0\n",
       "133      03-16        本科  19000  03-16本科  本科19000.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df2[:1], df2[-2:-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 39.将第8行数据添加至末尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>12500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科12500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test2\n",
       "134      03-16        本科  30000  03-16本科  本科30000.0\n",
       "7        03-16        本科  12500  03-16本科  本科12500.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.append(df2.iloc[7]).tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 40.查看每列的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    object\n",
       "education     object\n",
       "salary        object\n",
       "test          object\n",
       "test2         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 41.将createTime列设置为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16不限</td>\n",
       "      <td>不限27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科15000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           education salary     test      test2\n",
       "createTime                                     \n",
       "03-16             本科  27500  03-16本科  本科27500.0\n",
       "03-16             本科  30000  03-16本科  本科30000.0\n",
       "03-16             不限  27500  03-16不限  不限27500.0\n",
       "03-16             本科  16500  03-16本科  本科16500.0\n",
       "03-16             本科  15000  03-16本科  本科15000.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.set_index(['createTime']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 42.生成一个和df长度相同的随机数dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0\n",
       "0  5\n",
       "1  0\n",
       "2  9\n",
       "3  9\n",
       "4  1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame(np.random.randint(0, 10, size=df2.shape[0]))\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 43.将上一题生成的dataframe与df合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.concat([df2, new_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 44.生成新的一列new为salary列减去之前生成随机数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科27500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>03-16不限</td>\n",
       "      <td>不限27500.0</td>\n",
       "      <td>9</td>\n",
       "      <td>27491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科16500.0</td>\n",
       "      <td>9</td>\n",
       "      <td>16491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科15000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary     test      test2  0    new\n",
       "0      03-16        本科  27500  03-16本科  本科27500.0  5  27495\n",
       "1      03-16        本科  30000  03-16本科  本科30000.0  0  30000\n",
       "2      03-16        不限  27500  03-16不限  不限27500.0  9  27491\n",
       "3      03-16        本科  16500  03-16本科  本科16500.0  9  16491\n",
       "4      03-16        本科  15000  03-16本科  本科15000.0  1  14999"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['new'] = df2['salary'] - df2[0]\n",
    "df2.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 45.检查数据中是否含有任何缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.any(df2.isna())  # 只要存在缺失值就返回True\n",
    "np.all(df2.notna())  # 只要存在缺失值就返回False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 46.将salary列类型转换为浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.salary = df2.salary.map(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科27500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>03-16不限</td>\n",
       "      <td>不限27500.0</td>\n",
       "      <td>9</td>\n",
       "      <td>27491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科16500.0</td>\n",
       "      <td>9</td>\n",
       "      <td>16491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科15000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education   salary     test      test2  0    new\n",
       "0      03-16        本科  27500.0  03-16本科  本科27500.0  5  27495\n",
       "1      03-16        本科  30000.0  03-16本科  本科30000.0  0  30000\n",
       "2      03-16        不限  27500.0  03-16不限  不限27500.0  9  27491\n",
       "3      03-16        本科  16500.0  03-16本科  本科16500.0  9  16491\n",
       "4      03-16        本科  15000.0  03-16本科  本科15000.0  1  14999"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 47.计算salary大于10000的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.salary > 10000].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48.查看每种学历出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "不限      5\n",
       "大专      4\n",
       "本科    119\n",
       "硕士      7\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('education').education.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 49.查看education列共有几种学历"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.education.unique().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 50.提取salary与new列的和大于60000的最后3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科35000.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科37500.0</td>\n",
       "      <td>8</td>\n",
       "      <td>37492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>03-16硕士</td>\n",
       "      <td>硕士37500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>37496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education   salary     test      test2  0    new\n",
       "92       03-16        本科  35000.0  03-16本科  本科35000.0  8  34992\n",
       "101      03-16        本科  37500.0  03-16本科  本科37500.0  8  37492\n",
       "131      03-16        硕士  37500.0  03-16硕士  硕士37500.0  4  37496"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2.salary + df2.new) > 60000].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test2</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科14000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>13993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>03-16硕士</td>\n",
       "      <td>硕士37500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>37496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>29993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科19000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>03-16本科</td>\n",
       "      <td>本科30000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>29994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education   salary     test      test2  0    new\n",
       "130      03-16        本科  14000.0  03-16本科  本科14000.0  7  13993\n",
       "131      03-16        硕士  37500.0  03-16硕士  硕士37500.0  4  37496\n",
       "132      03-16        本科  30000.0  03-16本科  本科30000.0  7  29993\n",
       "133      03-16        本科  19000.0  03-16本科  本科19000.0  1  18999\n",
       "134      03-16        本科  30000.0  03-16本科  本科30000.0  6  29994"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三期 金融数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 51.使用绝对路径读取本地Excel数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "df3 = pd.read_excel(\"600000.SH.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 52.查看数据前三行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234  15.9855   \n",
       "\n",
       "     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n",
       "1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n",
       "2  46772653   838667398  0.1236  0.7795  17.9307  0.2507  3.376278e+11   \n",
       "\n",
       "         总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  1.865347e+10  6.6204  \n",
       "2  3.376278e+11  1.865347e+10  6.6720  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 53.查看每列数据缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "代码           1\n",
       "简称           2\n",
       "日期           2\n",
       "前收盘价(元)      2\n",
       "开盘价(元)       2\n",
       "最高价(元)       2\n",
       "最低价(元)       2\n",
       "收盘价(元)       2\n",
       "成交量(股)       2\n",
       "成交金额(元)      2\n",
       "涨跌(元)        2\n",
       "涨跌幅(%)       2\n",
       "均价(元)        2\n",
       "换手率(%)       2\n",
       "A股流通市值(元)    2\n",
       "总市值(元)       2\n",
       "A股流通股本(股)    2\n",
       "市盈率          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 54.提取日期列含有空值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>数据来源：Wind资讯</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              代码   简称  日期  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元) 成交量(股)  \\\n",
       "327          NaN  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "328  数据来源：Wind资讯  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "\n",
       "    成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)  A股流通市值(元)  总市值(元)  A股流通股本(股)  市盈率  \n",
       "327     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  \n",
       "328     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3['日期'].isna()]  # isna和isnull是一样的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 55.输出每列缺失值具体行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【代码】列的缺失值所在的行为[327]\n",
      "【简称】列的缺失值所在的行为[327, 328]\n",
      "【日期】列的缺失值所在的行为[327, 328]\n",
      "【前收盘价(元)】列的缺失值所在的行为[327, 328]\n",
      "【开盘价(元)】列的缺失值所在的行为[327, 328]\n",
      "【最高价(元)】列的缺失值所在的行为[327, 328]\n",
      "【最低价(元)】列的缺失值所在的行为[327, 328]\n",
      "【收盘价(元)】列的缺失值所在的行为[327, 328]\n",
      "【成交量(股)】列的缺失值所在的行为[327, 328]\n",
      "【成交金额(元)】列的缺失值所在的行为[327, 328]\n",
      "【涨跌(元)】列的缺失值所在的行为[327, 328]\n",
      "【涨跌幅(%)】列的缺失值所在的行为[327, 328]\n",
      "【均价(元)】列的缺失值所在的行为[327, 328]\n",
      "【换手率(%)】列的缺失值所在的行为[327, 328]\n",
      "【A股流通市值(元)】列的缺失值所在的行为[327, 328]\n",
      "【总市值(元)】列的缺失值所在的行为[327, 328]\n",
      "【A股流通股本(股)】列的缺失值所在的行为[327, 328]\n",
      "【市盈率】列的缺失值所在的行为[327, 328]\n"
     ]
    }
   ],
   "source": [
    "# df3['日期'][df3['日期'].isnull().values==True].index  # 可以\n",
    "# df3['日期'][df3['日期'].isnull()].index  # 可以\n",
    "# df3[df3['日期'].isnull()].index  # 可以\n",
    "\n",
    "for col in df3.columns:\n",
    "    if np.any(df3[col].isna()):\n",
    "        indexs = df3[col][df3[col].isna()].index\n",
    "        print(f\"【{col}】列的缺失值所在的行为{list(indexs)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 56.删除所有存在缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "\n",
       "     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n",
       "1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n",
       "\n",
       "         总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  1.865347e+10  6.6204  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dropna(axis=0, how='any', inplace=True)  # how='all'时，只有当当前行所有值都为nan时才删除\n",
    "# df3.shape\n",
    "df3.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 57.绘制收盘价的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MAID0lZLAJRgM6sCBA5KkjRs3JmSfmzZtktPpVF9fn+6///4Jt586dUo/+9nPJEk333yzioqKEvK4AAAAAAAgNezqloJspwqyXSlezewIV7gQuAAAkHZSErg0NzfL4/FIkpYvXx7x/R588EFdd911uu6667Rr165xtzU0NOi2226TJP3oRz/S3XffraNHj6qrq0tPPfWUPvjBD6qvr08lJSX69Kc/nbgvBgAAAAAApIQduFQX5qR4JbMn365w8QZkmmaKVwMAAMZKyeUfZ86cCX9cXV0d8f26u7t1/PhxSQoHNmPdddddOnXqlJ577jn98pe/1C9/+ctxt+fl5el73/ueamtrY1w5AAAAAABIFy1982t+izRa4RIwpRF/UDmh/wMAgNRLSYVLX19f+ONoApeZZGdn63vf+57+6Z/+SRdffLGKiorkdrtVV1enW265RY899pjOO++8hD0eAAAAgFHfePaoPv2LN+QLBFO9FADzhF3hMh8DF4k5LgAApJuEVLjU19fr4MGDEW//1re+NartbXfccYfuuOOOabcxDEM33XSTbrrppqj3DwAAACB2j+9p0aA3oMPtg1pTXZjq5QCYg0zTlGEY4f+32C3FiuZP4OJ0GMp2OTTiD2rIF1BpqhcEAADCUlLhAgAAAGDuGfZblS2dg94UrwTAXPTF3+3R+//rNfV6fOHPzccKF2nsHBcqCgEASCcELgAAAADi5g8EFQhaw5s7CFwAJJjXH9QLx7p0osujH7zUGP582zwNXOy2YoNef4pXAgAAxiJwAQAAABA3u7pFosIFQOJ1DY0+r/zmjdM63jkk0zTnbYVLnl3hwgwXAADSCoELAAAAgLiNPelHhQuAROscGm0jFjClbz53VD0en0ZCYe+CgvkVuNgVLkM+Woohs/kCQR1sG5BpmqleCgAkBIELAAAAgLgN+6hwAZA89vPKgoIsuRyGXjrRrUd2nZEklednKcs1v05vhCtcvFS4ILM98GqTPvjAdv3bc8dSvRQASIj59RcJAAAAgKQY9o+e9CNwAZBoXaHnlRULCvSB8+skST946aSk+ddOTJLywjNcCFyQ2Q63D0iSfrG9WX882J7i1QBA/AhcAAAAAMSNChcAydQZmuFSnpelj1+6SGV5bgWCVgui6nkYuOQywwVzRNeYdoH3/PchnegaSuFqACB+BC4AAAAA4ja2wqVj0EsvdgAJ1TVonZQty3erINulz16xJHzbfK5wGSJwQYbr9oSO7Ty3hnwBffmxfQSJADIagQsAAACAuI2tcPEGTA2McLIEQOKMrXCRpE3rqrWiMl+StLA0N2XrShVmuGCu6A5VuPzfG1apPD9LxzqHdO+fDnPhBoCMReACAAAAIG7D/uC4/3fQVgxAAtkzXMryrcDF6TD0jZvW6gtXLdWmtVWpXFpKhCtcCFyQwfxBU72hCpfllfn6h3euksOQ/rCvTY/taUnx6gAgNgQuAAAAAOJ2dvsP5rgASKTO0FXw5fnu8Oeqi3L0wQvrlRMKH+YTe4YLLcWQyXo9PpmSDEnFOW5dsLBEnwm1C3zwtaZULg0AYkbgAgAAACBuY1uKSVS4AEgsO8QtC7UUm+/y3NbpHGZdIJPZ7cSKc91yOgxJClesnez2aJjfbwAZiMAFAAAAQNxG/FS4AEiOYV9Ag6HWWRX5BC6SlJflkkRLMWS2bo/1t0Jp3mjlWnl+lopzXAqa0omuoVQtDQBiRuACAAAAIG5UuABIlq7QVfBZTkP5WfOvfdhkmOGCucCucCkbE7gYhqFzKvMlSUc6BlOyLgCIB4ELAAAAgLgNhypcQh1BqHABkDD280l5fpYMw0jxatJDbpZ1OocZLshkdphamju+cu2cCitwOdxO4AIg8xC4AAAAAIibXeFSXZgtiQoXAInTNcT8lrPZFS7McMFs6fH4dNvPXte/bDmSsH12D01sKSaNBi5HqXABkIEIXAAAAADEza5wqS3JlUSFC4DEGVvhAgszXDDb/niwXYfbB/XIrjPTDrP3BYJT3na2bk+owuXswCXcUowZLgAyD4ELAAAAgLh5QhUu9cU5kghcACRO5yRzHua7PLd1OsfjC8g0zRSvBvPBM4faJUn+oKn9rQOTbrN5b4suv+8F/ff+toj2OdkMF0laWm4FLp2D3nAVDABkCgIXAAAAAHGzr3atD1W49A775fVHfpUrAEyFCpeJcrOslmJBUxrhuRZJ1jXk1fam3vD/dzb3Trrd5r2tkqSfvnoqoiAwPMPlrHaBeVlO1YUu4DhCWzEAGYbABQAAAEDchkMn/BYUZsnlsIZad3FVKoAEsE/KEriMyg3NcJGkIea4IMmeO9Kp4Jj8ZOfpvgnbDPsC2hX6/OH2QR1sm7wKZqzwDJfcidVry2krBiBDEbgAAAAAiNtwqKVYrssZPilKWzEAiRCucKGlWJjDMJQbaivGHBckm91O7OpzyiVJu0/3KXhWBcvuM33yBUY/99ie1hn3O1rhMvHYXlZhBS5H26lwAZBZCFwAAAAAxG3Yb53wy3E7VBEKXDoIXAAkgF0tR4XLeHaVC4ELkqnH49NrJ3skSbdf2aBsl0O9w36d7PKM2+61U1absdpQK7Cn9rdN2+7O6w9qMPS7O9l8pnMq7AoXAhcAmYXABQAAAEDc7JMqOVS4AEgw+7mkLI/AZay80BwXDy3FkETPH+lUwJRWVOaroTxPa6oLJUk7T4+f42KHMh+9eKGqC7PVP+LXn490TLnfbo9V3eJ0GCrMdk243Q5cjnYMTqimAYB0RuACAAAAIG7DvtEKl/J860pVKlwAxGvIG5An1LKQCpfxwhUuBC5Ioi2HrXZi166olCRtqC2SJO1sHp3jMuQNaG9LvyTpokUletfaKknSY3taptyvPb+lLM8twzAm3F5fmqtsl0PD/qCae4YT8JUAwOwgcAEAAAAQN/uEaI7LGW4p1jnoS+WSAMwBdjuxHJcjXNEBS75d4UJLMSRJ37BP2xp7JElvWVEhSTrXDlxOjwYuO0/3KhA0VVOUrbriHL1rnRW4bGvs0Zm+ycMSe35LSe7ks5lcDkMNZXmSaCsGILMQuAAAAACI29gZLuXMcAGQIHY7MapbJqLCBcn2l6Nd8gdNLavI05JQ+GFXuJzs9qgnFJq8dtJqL3bBwhIZhqG64lxduKhEpqTNe1sn3Xd36L6TzW+xLatkjguAzEPgAgAAACAu/qApX8Dqr57jHlvhQuACID6d4ZOyBC5nsyt+hqhwQZJsORRqJ7a8Mvy54lx3uPLErnJ57VSPJOnChSXh7W4MVbls3tMy6QwWe4ZL6TTH9tg5LgCQKQhcAAAAAMRlxD96si/HRYULgMTpCle4TH0V/HwVrnAhcEESDIz49XJjt6TRdmK2DXVWlcuu070aGPHrQKs1v+WChcXhba45p0IF2U6d7hvR66FAZqyxM1ymsjwUuBxuJ3ABkDkIXAAAAADEZTg0v0WSsl2OcRUu5iRXtQJApGgpNrXwDBdaiiEJXjjWJV/A1JKyXC0tzxt3m91WbGdzn3Y09SpoSgtLclRdlBPeJsft1DtWLZAkPbZnYlsxe4ZL6RQzXKTRlmJNPR4N83sOIEMQuAAAAACIi30SJMflkGEY4dY//qCp3mF/KpcGIMPZJ2XLaSk2wegMl+AMWwLR+8M+KyS5dkWlDMMYd9u5ocBlf2u/XjphVcFcMKadmM0OXF492TPhNnuGS+k0FS7leW6V5LoVNKXjXUNRfw0AkAoELgAAAADiMuy3TvblhE7+ZbkcKs5xSWKOC4D42M8hZbQUm2B0hgvBNhKrpW9YL4eClHeuqZpw+6LSXJXmuuUNmHp8T4sk6aJFJRO2W7HAqlDpHPSqNzSzxRbJDBfDMHROqMrlCG3FAGQIAhcAAAAAcfGMqXCxlTHHBUACdIXmPFDhMtHoDBcqXJBYf9jXJlPS+fXFWliaO+F2wzDCbcXsiy7On6TCJT/LparCbEnSibMqVCKZ4SJJ54TmuBzpIHABkBkIXAAAAADExZ7hYp/8kzRujgsAxGq0woXA5Wx5zHBBEgRNU4+FqlZuXFc95Xbn1hWFP24oywu/7p+tITT/5VjnaOBimuboDJcZAxfr/lS4AMgUBC4AAAAA4jLaUmz07UU5gQuAOJmmqU57hgstxSbIC89wIXBB4uxo6lVz77Dys5x6y4qKKbezK1wk6cJJ2onZloYCl+NjAhePL6iR0N8OZTNUr1HhAiDTELgAAAAAiMvwJC3FKmgpBiBOg95A+KQsLcUmyg3PcCFwQeLYM1netrJyXOXq2VZVFcrtNCRJFy4snnK7hrKJgYvdKjDb5Zj2MSRpaUW+DEldQz61D4xE9DUAQCoRuAAAAACIi13hkj3mpAkVLgDiZT9/5Gc5lTPDSdn5yK5woaXY/GKapg62DsjrT/zsnoERv/50qEPS9O3EJCss+dgli3RFQ5kubyibcrvRlmKjFSo9Hqtybab5LZLVrnRtTaEk6ekD7TNuDwCpRuACAAAAIC7TVbgQuACIVVe4nRjVLZPJo8JlXvrVjtP64IPb9cOXGxO+7z8ebNeIP6iGsjytC4Uc0/nUZYt131+tmzYQtQOXtgGvBkb8kjRmfktkx/amUPjz+z0tMk0zovsAQKoQuAAAAACIy+gMl7EVLtZVq7QUAxArO7CN5Cr4+YgKl/nH6w/qp6+ekiS9dLw74fu324ltWlclwzASss+iHHf4IowTXVZbse6h6I7tt6+sVLbLoeOdQ9rb0p+QdQFAshC4AAAAAIjL5BUu2ZKkzkFfStYEIPPZcx6ocJnc2BkuXPU/Pzx1oE3tA9ZxcbhjMDzjKBGOdQ5q95l+OQ3p+jVVCduvNLatmBW4hCtcciMLXAqyXbp2RYUk6bFQKAQA6YrABQAAAEBchn3WCZ/cSSpc+kf84UAGAKIxWuFC4DIZu8LF1GilIeauoGnqwdeawv8PBE0dahtI2P4f39MqSbpiaXm4IiVRloYCl+OhwMWe4VIaRfWaPVPm6QPt/F0BIK0RuAAAAACIy7A/VOHiHn17UZjtUpbTakdiX8kKANHoDM9woaXYZHLcDtlNn5jjMve9eKxLxzuHlJ/l1Ma6IknSvgS11xr2BbR5rxW4bFqb2OoWabTC5fjZFS5RhKnn1RerrjhHg96AnjnckfA1AkCiELgAAAAAiItd4ZLjGq1wMQwj3AaIOS4AYkGFy/QchhGuLGSOy9z3QGh2y83n1ujiRaWSpH2tiQlcHt/bqh6PT7XFObpyWXlC9jnWaOAyKCn6GS6S9fu+aZ0VBp3dVqzH49O///mYXj/Vk4DVAkB8CFwAAAAAxGWyChdpdO5CJ4ELgBh0hStcCFymYs9xGaTCZU7bfbpPO5r75HIYuvX8Oq2pLpQk7T0Tf+ASCJp6KNSq7K8vqJPLYcxwj+gtLcuXJJ3uG5HHFxhT4RJd9do711TJkPT6qV419XgkSU09Hn3i4Tf04GtN+rdnjyZ03QAQCwIXAAAAAHEZrXAZ//aiggoXAHGww9ryKE/Kzid5oaCblmJz2wOhQOT61QtUWZCtNdUFkqTGbo8GRvxx7fvZwx1q7h1WcY5Lm0JzUhKtJM+t0lzrOD7RNaTuUOBSlhtdmFpdlKNLlljVPY/vbdXu03362M/f0MluK3w52jkkL/OMAKQYgQsAAACAuNjDmnPcznGfry3OkSTtbO6d9TUByGymaaor1HaICpepVRVmSxIzLeawxq4hPRf6+X7wonpJ1uyT2iLrZx/PHBfTNPWzUKuy959XG25Rlwx2W7FjHUPq9liBS0kMYeqNoVDot2+c1md/vUs9Hp9WLShQUY5LgaCpo6G2ZQCQKgQuAAAAAOJizw44u8Ll7asWSJKeO9Kp/uH4rsAFML/0j/jlC5iSohusPd989OJFkqRfv3FaJ0IDyZG5AkFTd/1+r975/ZfD/z728zdkSrpyaZmWlueHt7XbisUTuLx+qlf7WweU7XLofRtr413+tOzAZdfpPgWCoWM7N/rA5apl5SrOcal32K8Rf1BXLi3T9285V6sWWFU/B1sHErdoAIgBgQsAAACAuIRbip11ZeyaqgItq8jTiD+opw+2pWJpADJU16B1BXxhtkvZLk5dTOWSJaV609IyBYKmvvln5ldkukPtA3ruSKfaBrzhf/0jfjkM6WOXLBq3bXiOSxyBywOvWdUtm9ZWJT3YtMMie7B9QbZTWTEc21kuh96zoUaSdPO5NfqXd69VXpZTK+3ApY3ABUBquVK9AAAAAACZbdgfqnBxjz9xYhiGblxXrW8+d0yP7WnVzecm9+pZAHNHa/+IJKmM+S0zuvPqZXrpRLe2Hu/Wi8e7dEVDWaqXhBjZ1Rnra4r0d9cuC3++JNet6qKccdvGW+FyuH1AW493y2FIf31hfYwrjtzSUIVLY2jeSlkcAc9nr1yi926sDbfUkzQmcKGlGIDU4jIRAAAAAHEJV7i4JvZ+v371Ajkdhva19OtIOydBAMzMNE09+Lo1JHxVVUGKV5P+FpXm6pbz6iRJ9z13VP4AQ8MzlV2dcW5dkVZVFYb/nR22SNLqqkI5DKltwKuOgZEZ990x6NXp3uHwv/96xapuecvyCtWX5Cb2C5mE3VLMFks7MZvDMMaFLdJo4HK4fSDcsgwAUoEKFwAAAABxmarCRbJmL7x5WbmePdyhx/e26ItXL5uwDQCM9eLxLr18oltup6HPXLEk1cvJCJ+8bJH+sK9VJ7o8+s3OM7r1/LpULwkxsKsz7PBgOnlZTi0py9OxziHtbRnQVedkT9jGHzT13OEOPby9WbtO9026nw9etDC+RUeoLM8dnr0iSaUJrl5bWJqrHJdDw/6gTnV7tOSsgAcAZgsVLgAAAADiMl2FiyTduK5KkvSHfW3yceU1gGn4AkF987ljkqQPnF83K1fezwUF2S595solkqQfvNSoHo8vtQtC1AJBU4fbrQqXSAIXSVobbis2PkwZGPHrZ9tO6aYfbtP/3Lxfu073yWFIOS7HuH+b1laF95FshmGMq3JJdODidBhaXskcFwCpR4ULAAAAgJgFg6ZG/KHAZZIKF0m6dEmZKvKz1DHo1V+Odektyytmc4kAMsivdpzWyW6PyvLcE4aEY3rvXlet37xxWofbB/Xw9mZ9luqgjHKq26Nhf1A5LocWlUYWNK6pLtTje1u1r2U0YPD4AvrUL3bqSIdVLVOa69bN59bo5nNrVFEwsQpmNjWU5+mNZiscKo1jhstUVi7I1+4zfTrYNqB3rF6Q8P0DQCSocAEAAAAQM7udmDR1hYvLYeida60ql8f3tMzKugBknu4hr374cqMk6fYrl6ggm2tEo+F0GHrfxlpJsQ9SR+rYVRnLK/PldBgR3WdtTajCpbVfpmnKNE3d+6fDOtIxqLI8t+5+xwo9/ulL9DdXLEl52CJJDeX54Y/L4pjhMhW7MogKFwCpROACAAAAIGYe72jgku2a+u3FplDgsvV4l9ojGO4LYP753ouNGhgJaOWCAr1rbXWql5ORloZaNh3vHErxShAtOyRYEWE7MUk6pyJfbqehvmG/mnqG9ejuFv1hX5schvSP71qtTeuqp31tnm1Ly5LXUkySVlaNBi6maSZ8/wAQifR51gUAAACQcTw+K3DJdjmmvSJ3cVmeNtYVKWhKT+xtna3lAcgQh9sH9OjuM5Kkv71mWcRX+GM8e0ZGa/+IBkb8KV4NomEHLpHOb5Ekt9MR3v6RXWf0r88ckSTdfmWDLlhYkvA1xiuZM1wkaWm5VR3UO+xXaz8XdwBIDQIXAAAAADEbDgUuORFcQfuuUJXLs0c6k7omAJnFNE3927NHFTSlt66o0Hn1xaleUsYqynGrIt+ajdHYRZVLpjBNM6bARZLWVFltxR58rUnegKk3LS3Thy6qT/gaE6GyIEvFOa7Qx4lvcZbtcqghVEVzsG0w4fsHgEgQuAAAAACImccblDR9OzHb6tBJodO9w0ldE4DM8tyRTr12qldZTkN3vHlpqpeT8ewqgmO0FcsYrf0j6h32y2lIyyryZ77DGGuqC8Mf1xZl6+vXr5TDSM8KMcMw9H+uX6UvXbNMS8a0F0uklQus798h5rgASBECFwAAAAAxs1uK5bidM25bXWRdzdrj8YUrYwDMb15/UP/+52OSpA9eWK/a4pwUryjzMccl89jVGA3l+VHPXNlQWyRDkttp6N4b16goJ/GtuhLpiqVl+sD5dUnbvz0D5yCBC4AUcaV6AQAAAAAylyeKlmKF2S7luh3y+IJq7R/R4iRd3Qogczy8vVnNvcOqyM/SRy5elOrlzAl2hctxWopljEPhdmLRVbdI0sLSXP3be9aqJNcdriSdz1YSuABIMSpcAAAAAMTM47WGMkdS4WIYhqoLravXGWYLoGPQqx+/fFKS9Pk3NSgva+bnEcyMlmKZxw4HVkQ5v8V25dJyraspSuSSMpYduLT0j6jH40vxagDMRwQuAAAAAGIWTYWLJFUVWm3FCFwAfPeF4xryBbS2ulDXr1mQ6uXMGUvLrCqJM73D4edopLeD4QqX2AIXjCrIdqku1JqQOS4AUoHABQAAAEDMPN6gJCk3ggoXaTRwaSFwAea1A639enxPqyTpS9csS9sh35moJM+t0ly3TEmNtBVLez0eX/g1kcAlMWgrBiCVmOECAAAAIGbhChd3hBUuRVS4jOULBHXnI3tUmufW39+wSgYnnZHGeoZ8+t3uM3pyf5vK87N0y8ZavWlZuZyO0d/b/a39+sX2Zr1wrEv+gDnlvryBoExJ71hVqQ21tEJKtIbyPHU39epY55BWMdcjbTzw6ik9vqdV97xzVbh9mF2FUVeco4JsTtMlwsoFBXrmcAeBC4CU4JkcAAAAQMyGwy3Foqtwae0jcJGk/a0D2nayR5J0xdIyXb+6KrULAiZxpGNQv9jerKf2t2nEb1W1He8c0msne1RbnKP3b6xVVWG2frmjWW8090W836Iclz7/poZkLXteayjP0/amXh1njkva8PgC+tHLJzXoDejLj+/TAx88XwXZLtqJJYH9vTzQOqDeMXNcsl2OiGbOAUA8CFwAAAAAxMzjja7CpZoZLuOMvfr2/ueP6+pzKiJuzwYkW9A09a3nj+vB15rCn1tdVaD3bqzVqW6PfrfrjE73Duu+Px8L3+50GHrrigq999xaVRRkTbv/srws5WXx+54MS8vzJInAJY08c6hDg6HXzKaeYf2fpw7qn29cQ+CSBCsXWHOMGrs9eut3Xgp/Pj/Lqf993Upds7wiVUsDMA8QuAAAAACImd1SLDvaCpf+EZmmOe9baI0NXNoGvPrZtlP6myuWpG5BQMiwL6CvP3VQWw51SJLesrxCt11Qpw21ReHj9hOXLtKT+9v06zdOq9fj07vWVum9G2tVWZCdyqVDVoWLJB1nhkvaeGxPiyTprSsq9NyRTj13pFMPb2/WobZBSQQuiVRRkK3LG0q19Xj3uM8PegP6+pMH1VCepyVleSlaHYC5jsAFAAAAQMyGoqxwsQOXIV9A/SN+FeW4k7a2TGD37n/7yko9fbBdD7zWpHevr1Z1UU6KV4b5rHvIq799dJ92n+mT22no7nes1HWrF0zYLsft1Hs21Og9G2pSsEpMp6HcusK/qcejEX9Q2a7InqORHKe6Pdre1CuHId159TKdv7BE/7zliP7j+eMyTWvWkV2VgcT4979aL39wdI5UMGjqjt/u1vamXn3l8X36r9vOo70YgKTgFRcAAABAzEZnuET21iLH7VRJrhWyzPe2Yv5AUEc6rCubP3vlEp1XX6wRf1Dfev54ileWeq39IzrBlfkpcbLbo088/IZ2n+lTYbZL9793/aRhC9JbeZ5bRTkuBU3pZDfHUqpt3mtVt1y6pFRVhdl677k1eseqSgWCpoKmVJbnVgWVYQnnchjhf1kuh/7hXatVnp+lox1DuvdPh8NhFwAkEoELAAAAgJjZM1yimTtiV7m09M3vwOV415B8AVP5WU7VFufob69eJkPS0wfbtbO5N9XLS5kXj3XpfT95VR98YLta+oZTvZx552tP7NepnmHVFmXrRx/YqPPrS1K9JMTAMAw1lDHHJR0EgqY2722VJN24rlqS9fP5X29bEf4Z0U5sdlTkZ+kf3rlKDkN6Yl+bfr+7JdVLAjAHEbgAAAAAiJk9wyXSlmKSVD1mjst8Zs9vWbGgQA7D0MqqAt243joZ941njyo4D6+8/e3O0/rSo3vk8QU14g/qiX2tqV7SvNI/7Nf+Vuv38vu3nBueA4LMZP/8jhG4JMTeM33a29If9f1eaexW24BXxTkuvWlpefjzeVlO/cu71+gtyyv00UsWJnKpmMYFC0t0+5UNkqR/eeaIDrRG/zMFgOkQuAAAAACIWThwcUVf4ULgMnFQ8mevWKL8LKf2tw5o+6n5U+USNE196/ljuvdPRxQ0peWV1iyDx/e0zsvgKVX2h0481hXnMEdoDrADFypc4jfiD+qzv96lz/5qZ7iVZqQe32NVUVy3eoGyzmq/ubgsT/904xoqyWbZhy6q15uWlskbMPXdF0+kejkA5hgCFwAAAAAxG46lwqUo1FJs3gcuViXB2EHJ5flZunBhiSTpWOdgKpY16/xBU1974oB+9mqTJOlvLl+sH31go/KznGruHdaOpvkTPKWaffX+murCFK8EibCUwCVhuoe88viC8viCOtXjifh+PUM+PXekU9JoOzGknsMw9LFLFkmSDrfPj9daALOHwAUAAABAzOwZLlS4RCdomjoUDlzG9+5fWJorSTrVMz/mlzx9oE1/PNgup8PQ169bqU9etli5bqfetrJSkvTYHnrsz5Z9ocBlLYHLnNBQboW5J3s88gWCKV5NZuvx+MIfR/Pc/NSBNvmDplYtKNAK5rSklcVl1mtt+4BXg15/ilcDYC5xJWpH99xzjx544AHdc889et/73jfj9i+//LJ++ctfavv27ers7FR+fr7WrFmj97///br++utjWkMwGNQFF1ygoaGZr97Yvn278vPzZ9wOAAAAwNSGvNFXuIQDl3k8EP1077AGvQFlOUcHW9sWllitnE51R34VdSazA5VPXLJI71xbFf78jeuq9ejuFm051KG73uJXQXbC3r5iCvuocJlTFhRkKT/LqUFvQKd6PFpazjmQWHWPDVwifG42TTP8/LaJ6pa0U5TjVlmeW11DPp3s9mh1Fc97ABIjIRUuW7Zs0UMPPRTRtsFgUF//+tf1kY98RH/4wx/U0tIin8+nnp4ebd26VXfeeae+/OUvKxiM/uqL48ePRxS2AAAAAEiM4ThmuLQNeBUIzs/5HHY7sWUV+XI5x78tG61wmfuBS1OPR6+f6pUhadO6qnG3raspVENZnkb8QT19sD01C5xH2gdG1DbglcOQVlVxJf5cYBgGc1wSpCeGwOVg24AOtw8qy2noHasqk7U0xGFx6PW2sWvuv94CmD1xBy7PPvus7rzzzogDkm9+85t6+OGHJUnXXHONfvnLX+qll17Sr3/9a73pTW+SJD366KP66U9/GvVa9u3bJ0lyu93atm2btm/fPuU/qlsAAACA+HlimOFSUZAth2HN7uga8iZraWnNDlwmazGzsMQ6AdTcOyz/LAZSRzoGddfv9+pIx+z1s398b6sk6ZLFpROGtBuGEQ5hHqetWNLZ1S1Ly/OV6448QEV6syvojhG4xKXHM9pyKtIw/LE91vPb1edUqDjXnZR1IT6LQsdHYxfHB4DEiTlwCQaD+ta3vqXbb79dXm9kb5IOHz6sH//4x5KkG2+8Ud/5zne0ceNGlZWVacOGDfr+97+vDRs2SJJ+9KMfKRAIRLWmvXv3SpJWrVql4uJi5efnT/kPAAAAQHxM0xwTuER+gtblMFRZML/nuBycYn6LJC0ozFa2y6FA0FTLLLZd+/HLJ/XckU79y5Yjs/J4gaCpzeF2O1WTbnPDmio5DWnPmX4dncUgaD4abSdGdctcQoVLYvSMuTggksBlxB/UU/vbJFntEZGe7AqXE1S4AEigmAKXF154QTfddJPuv/9+BYNBrV27NqL7Pfzww/L7/SotLdXdd98th2P8wzudTn3kIx+RJA0ODqqxsTGqddmBy/r166O6HwAAAIDoeQOmzFABRo4rurcW4Tku8zZwscKDyQIXh2Gortiq9jg5S3Nc/EFTL5/oliRtb+rV3jN9SX/MV092q23Aq6Icl646p2LSbcrzs3Tl0nJJ0uOhq8WRHHtDgcta5rfMKfbcFgKX+IytcGkf8IYvNpjKn490qH/Er+rCbF24qCTJq0OsFtsVLt0cHwASJ6bA5ROf+IQOHjwot9utO+64Q/fdd9+M9zFNU08++aQk6ZZbblFh4eR/xF1//fXavXu3duzYoaVLl0a8JtM0tX//fkkELgAAAMBsGB5zwinWwKWlb/4FLh2DXnUOemVIWl45efX9InuOyywFLnvP9Kl/ZPSE4gOvNSX9Me12O9etWqDsaX5/7GHTf9jXKn8g+lmfmJlpmtrXYlVdrSFwmVPsmVDNvVzBH4+xM1ykmZ+bHwtV771rbZWcDiNp60J87AqXk90eBc35OVMOQOLFFLgYhqG3vvWt+v3vf6/Pf/7zEypVJtPU1KSuri5J0qWXXjruNr9/9A97p9OprKysqNd08uRJ9fdbV+SUl5fr3nvv1XXXXad169bpoosu0oc//GH9/ve/l8kTKAAAAJAQw37r5LfLYUwY/D6T6nlc4WK3E1tcljvlrAx7jkukswLi9eJx673aqlDFzTOHOpIa9vR6fHruSIekmdvtXNFQqrI8t7o9Pr1wrCtpa5rPTvUMq3/EryynoXMqaME9l1TkW+dXPL6ghrzRtW3HqLMDl6ZpnpvP9A1rW2OPJOldU7RLRHqoK86Ry2FoxB9U2zz8ewRAcsQUuDz55JP69re/rWXLlkV8n8OHD4c/Xrx4sXp6evTP//zPuvbaa7V+/XqtW7dOH/jAB/TEE0/EsqRwOzFJ+tznPqef/OQnOn78uHw+n/r6+vTKK6/o7/7u7/TpT39ag4P0/gUAAADiNTq/Jfq3FeEKl3l4guPQNPNbbPZV6bMVuGw9brUTu/X8Ol25tEympIdeT0yViy8w8UTvfx9oky9gakVlvlZWTT8zxOV06B2rFkiS/ny0MyFrwnj2/JaVCwqiDk+R3vKynMoLBbsdg5HN38VE3aHAxQ6wpmv3uHlvq0xJFy4qUV1x7mwsDzFyOR2qL7FaeDYyxwVAgrhiuVNDQ0PU92lvbw9/3NraqjvuuGPc54LBoLZv367t27frueee07333iunM/LBm2MDl5KSEt1xxx264oorlJeXpwMHDui73/2utm3bpueff1533XWXvvOd70y7P4OKz3Hs7wffFyA5OMaA5OH4ApJnJFThkuNyRn2MVRePVrjMt+Pz4JjAZaqvfWxLsWR/fzoGRsJruqyhVDXF2XrhWJc2723V31y+WGX50XcgsDV2DenOR/aopX9E71i1QLeeX6dVVQXhdmI3rq+O6Ou7clmZHt7erJdOdMuUKcc8+KWZzdcvO3BZW1M4747H+aA8362hnoC6hrxaXEYAIEV/fPWGApf1tUV69nCHTvVM/twcNE1tDrUTe/e6Ko6nDLC4LE8nujw60T2kSxtKU72cOYH3X5jvYgpcYjG2quSOO+5QX1+f7rrrLm3atEmlpaU6ePCg7rvvPr3wwgt67LHHVFtbqy9+8YsR79/j8ai4uFj5+fn61a9+pcrKyvBtl19+uS655BJ94Qtf0B//+Edt2bJFzz77rK655ppJ91VWli8nV/VMqrycfr5AMnGMAcnD8QUk3okB6wRUQY5LFRXRHWOrRqywpn3QG/V901lL77By3A6V5E0dUhwJDa++ePmCKb/2c93WW7XTfSMqLs2XO4nvT5450SNJ2lBfrJWLy7ViUZnO3XpSO0/1aPPBDn3p7Stj2u+rJ7r0qV/sVM+Q9XuyeW+rNu9t1bkLS3SwbUBZTof++oqlKo0g0HlrSZ7yfr9PnYNetXlNrasrimlNmWg2Xr8OhX4nL1leOaeOR1hqSvJ0qmdYIw4HP9+zRHJ8BYOmeoetVviXLa/Qs4c71DLgm/R7ufVIh073jagwx6X3XdagnCnaRiJ9rKor1p+PdKrN4+f4SDDef2G+mrXAxeMZLc3r6OjQD3/4Q1155ZXhz61fv17/+Z//qU9/+tN64YUX9KMf/Ui33Xabqqoi63d599136+6775bf75fLNfHLcjqduvvuu/Xcc8/J5/Ppt7/97ZSBS1fXICnsWQzDeqLs7OwXY3CAxOMYA5KH4wtIntZOqyrC7TDU0dEf1X2zA9bJq/b+EZ1u6VXWNEPT013QNPXyiW794vVmbT3RrbriHP32ExfJNcmg5IERvxpDJ7ercxxTft+cpqlsl0Mj/qB2H+sIV7wkw9O7TkuSLq4vDq/ntvNqtPNUj3669YTet65KuVnRnTR8+kCbvv7kQXkDptZWF+ozVyzW43tbteVQh3ae6pEkXXVOuQKeEXV4Imsrd+HCEj1/tFNP7GhSdXbm/r5EarZev/yBoPY090qSFua7oz6Wkf6Ksqzj5URLnzpqOQEqRXd89Xp8CgStjZYWWdWZx9oHJj1WHnjxuCTp7SsrNdA7pIHELhtJsCDHOod44HQvz38JwvsvzGWRBLOzFrjk5o6+Qbj66qvHhS02p9OpL33pS3rhhRfk8/n0zDPP6AMf+EBUjzNZ2GJbsGCB1q9fr+3bt2vnzp3T7ocnhMmZJt8bIJk4xoDk4fgCEi88w8XliPr4Ks5xhwOF1v4R1ZekX5ubE51DMgyr3chkTNPUo7tb9PPXm3RiTO/35t5h7Wru03n1xRPuY7fuqirMVnGOe8rvmyFDC0tydaRjUCe7PVqYpO+PP2jq5UZrfsvlDWXh9Vy1rEILS3J0qmdY9/35mNbXRF5RcrxrSD/ddiq0n3Ld885VynE7demSMv2PN4/oN2+c1p6Wfn3qssVR/d5c0VCq5492auuxLn38kkWR3zHDJfv160jHkEb8QRVkO7WwJJfXyjmoPFRF1j7g5ed7lkiOr+5QlV5+llMN5dbrQcegV4MjAeWNCaP7h/165nCHJGnTumq+1xliceiChsYuDz+zBOP9F+arWQtc8vPzwx9feumlU263Zs0a5eXlaWhoSEePHk34Ompra7V9+3Z1d3cnfN8AAADAfDLsC81wiaFlimEYqirM1sluT1oGLn3DPn305ztkGNLjn7pEBdkT3zpt3tuqf/zjYUnWibhN66rV2DWkl05068XjXZMGLnvPjA4nn8nCUitwOdXtkaIfoxmR3af7NDASUHGOS2uqR6/YczoM/fWF9br3T0f0251n9NudZ6Le9y3n1eqLVy+Tc0ylT1Vhtj73pti+mMsbyqw1n+lTr8en4lx3TPvBeHtD81tWVxXOi9k485EduHQOelO8kszUE5rfUpLrVlGOW8U5LvUO+3WqxzPuufyZw+0a8Qe1rCJPa6pmfo5HerAvqmjtH5HHF1AubeAAxGnWApf6+vrwx9nZ2VNuZxiGCgoKNDQ0pOHh4agfxzRNGdP8kejzWS+UYytuAAAAAERveEyFSyzGBi7p5uUT3Rr0Wl/fC8e6dN3qBRO2+cP+NknSTeur9YWrlqog26Un97fqpRPd2nq8S5+fJFh49nCnJOmSxSUzrmFhSY4kWYFLkrx4vEuSdOmS0nHBiCRtWlutw+2DOt0b3fsyh2HoLcsrdOP66oStU5Kqi3K0tDxPxzqH9Epjt96+auLPZDa8fqpHLoehc+smBmrpzuML6Mn9bdpYV6Sl5dZFkftCgcvaalpNzVUVocClg8AlJj0eqwVmSSjkXVSaq91n+tV0VuDywjHr+fTaFZXTnpdCeinJHQ3RTnZ7IrogAgCmM2uBy6pVq8Ifnzx5csrt/H6/enut/rGRzm9pbm7WRz/6UXV0dOhTn/qUbr/99im3PXLkiCRpyZIlEe0bAAAAwOSG/bFXuEhW4CIpLQOXrSdGK+K3HGqfELh0D3m1PTSL5CMXLwxXwFy2uEyGpMPtg2rrH9GCwtGLzVr7R7T7TJ8k6ZrlFTOuwW4jdrIneYHL1lDgYlePjJXlcugrb12etMeOxeUNZTrWOaStx7tSErgMjPj1P367W0FTeuQTF6mmKGfW1xCP+547pkd2WdVKly4u1a3n14UDlzUELnMWFS7x6fFY37fSPCtwqS+xApeTY8JwXyCoV0/2SJKumOT5FOltcVmedp3uU2PXEIELgLjN2qTB8vJyrV+/XpL09NNPy+/3T7rdq6++qpER6w3Xxo0bI9p3VVWVOjs7NTQ0pOeff37K7fbt2xduU/bmN785itUDAAAAOFsiKlwkqaUvvQKXoGnqpVAQIUkvnejWUKjaxfbckU4FTWnVgoJx7dBK8txaW1MYul/XuPs8G+rtf25tkSoLpq76ty0M9ZVviiFw6RgY0b89e1T/8PShcf9+88bp8NfS1j+iw+2DMiRdtqQ06sdIBftE5ksnuhU8qzH80wfa9MTe1qQ+/sluj7wBU/6gqYdfb07qYyVa56BXm/e2SJIMSS83duvO3+3R4fZBSVS4zGUVBC5xsStc7DaG9nPz2OrDnc19GvQGVJbn1iraiWWcsXNcACBesxa4SNItt9wiSWpqatL3vve9CbePjIzoX//1XyVJ1dXVuvzyyyPar8vl0g033CBJ2rFjhzZv3jxhm8HBQX3ta1+TJOXl5enWW2+N6WsAAAAAYIlnhoskVadphcvBtgF1DfmU53aqtjhHI/5guPWW7ZlDVnjylhUTK1UuX2KFAi8eHz830h6mPNl9JrModALoTO+w/IFgVF/D915s1MPbm/Xo7pZx//5pyxG98z9f1jefO6rf77ZOvq+pLlRpXlZU+0+Vc+uKlOd2qmvIpwOtA+HPP3O4Q1994oD+z1MHk3pSeWz49ejuM+ob9iXtsRLtVzua5Q2YWl9TqN998iLddkGd8kMDv2uLc8ZVY2FusStcuod88geZYB2t8AyXnFBLsVDIfmrM84FdLXjZklJmIWWgJaE5Lo3dQyleCYC5YFYDl5tvvlkXXXSRJOlb3/qW/uf//J/au3evuru79corr+hDH/qQ9uzZI0m6++675XSOf+P24IMP6rrrrtN1112nXbt2jbvtjjvuUElJiSTpK1/5iv7jP/5Dhw8fVldXl5555hndeuut2rt3ryTpq1/9qiorK5P81QIAAABz22hLsRgrXIpCFS790c9uTCb7xNnFi0v01hXW+4ZnDrWHb+/x+PTqSStMecskrcEuX2oFLtsau8NBScegV2809U55n8lU5Gcpx+VQwJSax8xRCQRN3f+X4/rdrskH2Q95A/rjQWu9t55fp89esUSfvWKJPn7pIi0qzdXASEA/f71Z//lSo6TMan/jdjp0cWj+jf1zOtnt0f996qAkydT471Wijd23xxfUb3dO/jNIN0PegH4TWusHL1qouuJcffHqZXriby7RPTes0jduWpviFSKZSnLdchrW8dE9RJVLtOzAxW4pFq5w6Rl9PnhxmvaMSH+Ly6hwAZA4szbDRZIcDoe++93v6nOf+5xeeeUVPfLII3rkkUfGbeN0OvV3f/d3uvbaayfcv7u7W8ePH5ckeTzjnwSrqqr0gx/8QLfffrva29v17W9/W9/+9rfHbeN2u3XXXXfpve99b4K/MgAAAGD+ibelWHWhNf8i3SpcXjxmhSmXN5Rp5YIC/ezVU3rhWJeGfQHluJ16/minAqa0vDJfi0NXxY61uqpApbludXt82nm6TxcsLNFzhztkSlpXU6jqCOd+GIahhaW5Otw+qFM9nvBjbTnUrp9uOyWHYYVCdcW54+635VC7hnwBLSzJ0ZeuXjpuePPfXL5YW4936Rfbm/VKY48chnTVOeUxfqdS44qGMj13pFNbj3fpgxfW6yuP79PgmJZvLX3D2lBblJTHbg6dYG0oy9PxriH9YnuzbrugXtkxHgOz5fd7WtQ37Nei0lxdtWz0552f5dI7Vs/+LBzMLqfDUGleljoGveoc9EbU0hCjwhUuudYpNHu+VuegV4Nev/qH/TrWOSSHIV2yODPaM2K8xaWjFS6maY573QSAaM36X4WFhYX66U9/qn/913/VlVdeqdLSUmVlZam+vl433XSTfvWrX+mjH/1oTPvesGGDNm/erDvuuENr165VXl6esrOztWjRIt1yyy363e9+p4985COJ/YIAAACAeWq0wiW2lmL2DJeBkYDa0iR06fH4tLfFGmx/eUOZVlcVqKYoW8P+oLaesIIYu53YtVO0BnMYhi5rsE662VUYW+x2YhFWt9gWloy/kto0TT3wapMkKWhq0jkij++xWoVtWlc94aSRwzB05dJy3f/eDfr1Ry/UT//6PK3IsAHBl4WuIN9zpl9ff+qgDrcPqizPrYsWlUiSziRxJlBzr3Xh3wcvrFdVYba6hnz6w77kzo2Jlz8Q1M9fs35n/vrCejkdnEicj+w5Lh3McYnaaOBiVbgU5rjCHzd1D4ef59fVFIXnvCCz1JXkyGlYlYttAxwjAOKTkAqX+vp6HTx4MOLtDcPQpk2btGnTpqge54477tAdd9wx7TYlJSX6/Oc/r89//vNR7RsAAABAdDxxVrjkZTnDlQKf/uVO3fdX68J91FPllRPdCprSORX54UDoLcsr9dDrTXrmULsuWliiVxqt4OXa5VO3Kb58SZn+sK9NL4aqMLaf6rH2FeH8FtvZw5lfPdmjA20DchhW4PLo7hZ98tLFKgm1umnsGtKO5j45DOmGNVXT7ntJeWq/17GqKszW8sp8HW4f1JZDHXIY0t/fsEqvnerRqyd7dKYveS3FmkLB1+KyXN12QZ2++dwxPfhak25cV522QcafDnWopX9EZXluvXOG3wnMXfYcl2TOOJqrzg5cJCsM7/H4dLLHE57XlUntGTGe2+lQXUmuTnZ71Ng1FH79B4BYpHfdMwAAAIC0NeyLr8JFkv753WtUV5yj5t5hffLhN7QjNOckVbaesPvwj7aFeetKKyT5y9Eu/elQu/xBU0vL86YNLC5ZUiqHIR3tGNIvd5xW0LRajZ3d/msm4eHMocDFrm5577m1WrWgQCP+oH6983R4+817rWqLS5eUzukTRpctGT2x+TeXL9HFi0vDrdpaklTh4vUHw+3v6kty9e711SrMdulkt0fPH+1MymPGyzRN/ezVU5KkW86rS/vWZ0geKlxi1z00MXBZVGo93xzrGAzP9Br7uoHMsyh0gUNjd+RzXFr7R/S5X+/S3z66NzyzDQD4awsAAABATIb9oQoXd+xvK5aU5enHt23UuppC9Q779bnf7NLTB9oStcSoBE1TLx0fnd9iW1tdqKrCbA35AvrOCyckTd1OzFaS69baamuOiH3CO9p2YpJUHzqpd7LHo0NtA3q5sVsOQ7rtwjp96KJ6SdKvdpzWsC8gf9DUE6H2Vjeuq476sTLJ9asXyO00dM3yCn30koWSpJoiK2BKVoXLmb5hmZJy3Q6V5bmVn+XSzefWSJIeePWUTNNMyuPGY1tjjw63DyrX7QivFfNTeb4VFnQO+lK8ksziCwTDM6LGBi71oTD8iX2t8viCKs/Pyrj2jBjPrrBt7BqKaPuDbQP62M93aNvJHj1/tFO/2XkmmcsDkEEIXAAAAADExK5wyY2jwkWSyvKy9N33bdDV55TLFzD11ScO6Eu/26Ntjd2zehJ7f+uAuj0+5Wc5de6YoeuGYYTDEru1zFtWTN1OzHbFUutqZ1/AjPg+Z7MrXFr6hvWTV05Kkt66olJ1xbl6y4pK1RZlq8fj0+a9rXrlRLfaB7wqznHpTUvLp9ttxjunMl/PfO5y/dOm1XKE5tTUFI5WuCTj96ap1wpy6opzw7Nxbjm/Tm6nod1n+rXnTH/CHzMePUM+/cfzxyRJ715fw2yJea483wokqXCJTm/oOd9hWLNbbHY1hD0z6rIlpeHnImSmxVFUuLx0okuf/sXO8GuuJP3gpcbw3wgA5jcCFwAAAAAxCVe4JKBNUY7bqXs3rdFtF9RJkv5yrEuf+81ufeBnr+vRXWc0HJoXk0z24OOLF5fK5Rz/NY2taFlcmqtlEcw/GVsls7wyP3yCLhrl+VnKczsVNK1ZHJLClS0uh6HbLrA+fuj1Jj2627q69vo1VcqaB62jctzOcPAhSdWhCpchX0D9I/6EP15zjx245IQ/V5GfpauWWb8bL4dm+6SDk90effzhHTrUPqiiHJf+OnRcYf6qCFe4ELhEo8djPZcU57jHBSoLz3o+Z35L5lscYYXLo7vO6IuP7NGQL6ALFxbrNx+/SMsr89U37Nd/bm2cjaUCSHNz/69wAAAAAEmRiBkuYzkdhr549TL9+mMX6n0ba5Xrduhox5D+4Y+H9a7/fEXfeeG42vqTM59DGg1crpikD//62qLwDIRrV1SMO9E/lZULClQWGmY/UwuyqRiGofqS0RP8Fy0q0aqqwvD/b1xfreIcl5p6hvXcEWuOyLvneDuxqeS4nSoNVXGcScIcl+Ze66rnujE/D0k6r96qhtrV3Jfwx4zFzuZeffznO3SqZ1i1Rdn64a0bw/NtMH+VM8MlJt0e6/tVkje+QmxhyWjg4jSkSxYzvyXTLS6zK0pHNDBFaL/1eJf+4Y+HFTClG9Ys0H/cvF4luW596eplkqRHdp7WkY7BWVszgPRE4AIAAAAgJsP+UOCS4GqKJWV5+rtrz9ETn75UX7hqqWqKstU77NdPXjmlG3+4TV97Yr/2nEnsye2eIZ/2hlpCjR3IbnMYhm6/cok21hXp5nNrI9qnwzD0N1cs0QULi3XT+tjnZ4y9kvrDoeoWW67bqfdtHF3P6qoCnVOZH/NjZTq7yqUlCXNcmkIVLvUl469sP7e2WJK0+0yfAsHUznHZcqhdt/96l3qH/VpdVaAf33aeGiKoxsLcZwcunYPetJw3lK7sCpeSs1ryFWS7wgHvhtqice3GkJlKc91aXJorU9Zz6WR+/cZpSdKmtVX6+nUr5Q5Vw164qERXn1OugCl989mjHGPAPEfgAgAAACAmdpuvRFW4nK0wx6UPXlivRz5xsf7pxjU6r65IgaCp/z7Qro/9/A19/Oc79PSBNvkDwbgf69VTPTJltf5aUJg96Tab1lXrB7dunPL2yfzVhhp97/3nhk92xsJuRba8Mn/Sq6jff16tskOh143ztLrFVhOq5EhqhUvx+GqRZZX5ynM7NegN6Fhn6q5sPtY5qP+1eb+8AVNvWlqm798S3+8d5ha7Qm/EPzoEHjOzZ3KcHbhIo8/Nl9FObE4wDCP8GvrYntYJt7cPjIQrYT980cIJla5fuGqp3E5D20726PmjXclfMIC0ReACAAAAIGqmaY4GLkmeF+JyWEPr//PWjXrwg+frnWurwoPKv/rEAb37h9v0s22n4qouOBHq2b66qiBRy06Y92yo0bUrKvS1t6+YtJVZaV6WvvLWc7RpbZXetbYqBStMH3aFy5kEV7iYphme4XJ2hYvLYWhdjdXmbWcK24o98GqTgqZ0eUOp/uXda5WbpCAUmSnH7VR+lvU7QVuxyPUMWYFL6SSBy6cuX6wb1izQzefGXsGI9HLDmgVyGtKu03060Tl+lssf9rUpaFoVTUsmqRysL8nVB863qlD//c9H5fXHfzEIgMxE4AIAAAAgav6gqUAo38hxz97bipVVBfr6dSv1+Kcu0acvW6yyPLfaBrz61l+O66HXmmLeb3OPVb1w9sn0dFBTlKN7N63RmurCKbd519pq3X3dyqRVG2UKu8KlJcEVLp1DPg37g3IYUk3RxAqnc+usOS47T6cmcGnrH9FT+9skSZ+6bLGcjplnDGH+qRjTVgyRGa1wmdgy7JLFpfo/169SUc7EMAaZqaIgW5eHKpYe39sS/rxpmnpsj/X/G9dNfWHDxy9dqLI8t071DOsvxzqTu1gAaYvABQAAAEDUhn2jV26m4kr68vwsferyxeHgRZJ+vr055itKm3ut6oWz20Uhs9QkqcLFDuSqCrPDPfvH2lBrBS67mnsT+riR+sX2ZvmDps6rL9a6mqKUrAHpr5zAJWp24FI8SYUL5ia7rdjmva3hlqW7TvfpZLdHOS6H3rqycsr75me5woGNXTkLYP4hcAEAAAAQtWG/1U7M6TDkSuHV9Fkuhz56yUItKMhS56BXT+6f2Hc9EvZA9Lo0rHBB5KqTVOESDuSm+P1YV1MkhyGd7htR+0Di58dMZ2DEr0d2nZEkfejC+ll9bGQWu8KFlmKRm26GC+amK5eWqSzPra4hn7ae6JYkPR6a6fLWlZXKz5pY7TRWfYn1OmT/XQFg/iFwAQAAABA1u8Il1+2cdK7IbHI7HfrABdaJ5gdfa1LQjG6Wy7AvED4BWU+FS0azK1y6Pb7wjKFEaApVuExVAVWQ7dKyinxJ1pXQs+l3u85o0BtQQ3merljK8G5MjQqX6HWHApfSPAKX+cLldOj61VbbsMf3tGjIG9AfD7ZLGq1+mU5dsRXM20E9gPmHwAUAAABA1OwKl3SZGXLT+moVZDt1osujvxztiuq+9kmRgmyninKmv3IV6a0w2xUeDJ7IKhf7SuXpArlzQ23FdjbPXuDiCwT18PZmSVZ1iyPF4SfSGxUu0eulwmVe2hSa0/KXY1369RunNeQLaGFJjjbWzdyy0a5wsVtRAph/CFwAAAAARM1jV7hkpcdbioJsl/5qQ60k6cHXTkV139GT6bkpr9ZBfAzDULU9x6U/cVcXz9RSTJLOrSuWJO2cxQqXp/a3qX3Aq8qCLF23esGsPS4yExUu0TFNk5Zi89SyinytqylUIGjquy+ekCRtWlcd0d8I9aEKl7YBb0IrLQFkjvR4dwQAAAAgowx5/ZI0Yy/z2XTr+bVyOw290dynnVEML2/uta5Cta9KRWarLrR+jmcSWuEy8+/IhlCFy8G2gVk5yRY0TT3wWpMk6QPn18nt5O09pkeFS3Q8vqC8AatFJYHL/LMp1D4sEDTlMKR3rqmK6H7FuaOVlol8HQKQOfiLDAAAAEDUejxW4FKal5XilYyqLMjWDaG+6w+GTkRHojlU4VJbPHX1AjKHXeHS0peYChePL6CuIesq9/ppfkdqirJVWZClQNDU3pb+hDz2dF463q3jnUPKz3LqPRtqkv54yHyjFS6+FK8kM3R7rGAq2+VQbpq0z8TsefvKSmW7rNOmly0p04LC7IjuZxhGeN5XE23FgHmJwAUAAABA1Ow2K2UF6RO4SNJfX1gvSfrzkU4d6xyUPxAM/zNNc9L7NFHhMqfUFCW2wsUO5IpyXCqcZsaPYRjhOS67ZqGt2LaT3ZKk61YvUEF2+lSaIX3ZFS49Hp98gWCKV5P+7AsLqG6ZnwqyXXrPhho5DKuKMBp2+0m7HSWA+YW/ygAAAABErdsOXNKowkWSGsrz9OZl5Xr+aKdu+a/Xx922rqZQP7h1o1yO8T3Y7RPqddMMREfmqJmiwuV455A+95tdev/GWn30kkUR789uORfJ78eGumL96VCHdjYnP3Bp7LLWtaIyP+mPhbmhKNclp8NQIGiqa8inqgiv2J+vmN+CO69aqo9fsjDqat76KCtcfvzyST26+4y+874Nqp9mVhiAzECFCwAAAICo9YZORJXmp1fgIkmfvGxRuA3IWHvO9OtYx+C4zwWCpk6HTsxzkmNuqJ6iwuW3O0+rfcCrB19rkj+Kq/ubwoHczL8fYytcglNUVCVKY/eQJGlxWV5SHwdzh8MwVJ5nhQeZNMclVYPHe0KtBEsJXOYtp8OIqXWqXTEbaYXLb3ee1pm+Ef1hX2vUjwUg/RC4AAAAAIhauKVYXvqdiFpdVagtn7tcWz53WfjfefXFkqR9Z83WaB8YkS9gyukwIu7PjvRmV7i0D4yEg5WgaerZwx2SpN5hv14/1Rvx/uwrlCNpObeiMl85Lof6R/w63jkU7dIj5vUHdTp0Im9xKUEhIjc6xyUzApd///MxveXbW/XIrjOz/tj261xxLs1hEB07oLcraKfTMTCitgHreHzxeHdS1wVgdhC4AAAAAIja6AyX9Awpsl0OFeW4w//W11iVB2cPM7evPq0typ7QagyZqTw/S26noaCp8EmsPWf6wx9L0pbD7RHvz/4diSRwcTkdWltTKEnamcQ5Lqd6PAqaUn6WM3wCHYiEPcdlbIWLaZo60TmkQDC5VVnRemp/mx58rUm+gKl/2XJEe88kv1XfWLQUQ6zqwhUunhmrHfe2DIQ/3t/Sr66hzAhDAUyNwAUAAABA1LqH0nOGy1TWVhdImljhYlcv1NFObM5wGEZ4NsWZULu4LYesgKU21Ff/ucOd8kd4ctkOXCJpKSaNbyuWLI3d1u/totJcGQZBISI3WYXLD18+qff912v65Y7mVC1rgmOdg/rHPx6SJC0oyJI/aOorj+8PhyCzgcAFsaouzJbTkLwBUx0D0wco+1pH/y4xJb18gioXINMRuAAAAACIWk94hktmnIhaU21VHRztGBw3D2D0ZPrM1QvIHPYcl5a+EZmmqWcOWe3EPv+mBhXnuNTt8emNppnbigWCZrh1VyQVLpLV0k6SDrcNzLBl7Bq7rHZlS5jfgihVnBW4NPd69F+vnJQkvXqyJ1XLGmfIG9BXHtsvjy+oixaV6OGPXKBFpblq6R/R/37yQNLnI9nCr3Np2DoT6c3ldIRfh5p6PdNua18IYh+bW493JXdxAJKOwAUAAABAVEzTVK/dUixD2hlVFWarLM+tgCkdHHMi3B6IXk+Fy5xSM6bCZV/rgFr6R5TrduhNS8t09TkVkkarXqbTNjAif9CUy2GoMsL2eedU5kuSTnQNRVxFEy27wmVxGb+3iM7ZFS7fev64vAHr9/RI+2DK1mUzTVP/+MdDOt41pMqCLN3zzlUqynHr3k2rle1yaOvxbv3XK6dmZS1UuCAedkg/3RwX0zS1PxS4fPjihZKsCpd0a+8HIDoELgAAAACi0j/iV+j8nEozpKWYYRhaG6py2dc6GrhQ4TI31YypcLGrW65oKFeO26m3rLACl2ePdM54UutYh1VJUlucI2eEM35qi3OU63bIGzB1qnv6K5un8+cjndpxcvLWMnaFy+JSKlwQnbEzXF4/1aMthzpk/2q39I9oYMSfwtVJv915Rv99oF1OQ/rHd64Ot61cXlmgL197jiTp+1tPaFtj8tsuEbggHnYbyqbeqQOX5t5h9Q775XYaes/6ahVmu9Q77J8wbw5AZnGlegEAEq9/2K/H97ZoxB9M9VIyTkV+lq5fU8XQXAAAptHjsU7I5bmdynE7lbzGSYm1prpQfznWFRq8XCdJag7NcIm0XRQyQ3XRaIXL6009kqRrQ0HLRYtKVJjtUuegV7tO9+m8+uIJ999zpk8Pv96sLYetsGZRaeSVJA7D0LKKfO05068jHYNqKI8+FHlib6u+/tRBFeW49PRnL5XTMXqtpGmaauyiwgWxsStc2vpH9G/PHpUkvWdDjf5ytFNtA14d7RjUuXUTj4nZ4A8Edf9fjkuSPv/mpdp41rG5aV21djb36fd7WvS9F0/o4sWlSV2PPausmMAFMRitcJk6eN97xgpWVlQWKMft1CWLS/WnQ+168XiXNoTmgQHIPAQuwBz0nReO6zc7z6R6GRmrPD9LlzeUpXoZAACkrdGrfjPr7cTaGqvCZX+owqV/2K/eYSs8inQgOjKDXeGy83SfRvxBZbsc4b/v3E6H3nxOuZ7Y26oth9rHBS5vNPXq358/pj1nRq8uPq+uSJ+5YklUjz82cHnbysoJt+8+3aeXTnTptgvqVZA9/jg60j6o//enw5KkvmG/DrcPalVoLowkdXt86h/xy5C0kFZ4iFJFQShwGfCqbcCrgmyn/ubyxTrTN6y2Aa+OpDBw2dc6oEFvQMU5Lt12Qd2k23zk4oX6/Z4WHWwbkD8QlMuZnMYtgaCpvtDrQymBC2JgV842T1Phsq/Veq2xK3CvWGoFLi8d79JnZ3jd2X26T6+f6tGHLloYcQUmgNmRWe+QAMxoxB/Ufx+w+lFfu6JCBVkc5pHaeqJL7QPe8EkkAAAwuXDgkiHtxGz2MPOT3R71DfvCw9DL8tzKy3KmcmlIMLvCxa74vryhbNzP+NrlFXpib6uePdyhL12zTA7D0B/2terv//uQ/EFTbqeht69aoA+cV6eVVQVRP/45FdYcl6lmYvy/Px3W4fZBPXu4U998z9rwcOVBr19ffnzfuEr1naf7xgUudnVLTVG2ctz83iI6ZWc9b3/qssUqzcvSORX52nq8W4dTOMfl9VM9kqTzF5bIYUx+ArmuJEf5WU4NegM60eUJz0xKtP5hv+yGg8U5vKdG9OpCgXjTNDNc7AqXNaHA5bIl1oUB+1sH1DHoDbcAPNuIP6i7HtunzkGv6kty9dZJgn0AqcOrBjDH/OVop/pH/KoqzNY/vmv1lH+oYqIvPLJb7QPepA03BQBgrugZyswKl5Jct+qKc9TcO6z9LQPqH6G6Za6qKsyWIYVPmF67vGLc7ZcsLlV+llNtA17tPt2n10716HsvNlrbrqjQXW85J9x6KRbhwKVj4snrgRF/OIg50jGojz/8hr75nnVaUZmve/77sE52e7SgIEvXrqzUw683a2dzn245b/Rqf3t+y6Iy5rcgetkuh4pyXOob9mtRaa7et7FWksLBxdFJfmdny2sneyRJFy6cusLGYRhaUZmvHc19Otg2kLTAxb6woDDblbQqGsxtdoVLj8engRH/hGpGf9DUgTar4taucCnPz9LqqgLtbx3Qyye69K611ZPu+8l9reoc9EqSXj3ZQ+ACpBleNYA5ZvPeVknSDWsWELZEyRXqjT3T8FQAAOa7TB4kbJ/U2NvSH27zUcf8ljnH7XSoMtQ6Kctp6Iql49vFZrkcetOycknS/9y8Pxy2fOjCev3ju1bHFbZIo4HL6d5hDXrHDyHffaZPpqQFBVlaWp6n9gGvPv2LnfqHpw/rT4fa5XQY+n+b1uiq0Pp2NvfKNEf/Pj1hz2+JYq4MMNay0O/nl65eJncoTBgbEo79fZstXn9QO0/3SZIuXFQy7bYrFlhVZwfbkjdBrDv0Oleal3mvc0gPBdmu8N9Jk7UVO945qBF/UPlZTi0aM4/rslD7yxePdU+636Bp6sHXmsL/fy1UGQYgfRC4AHNIx6BXL5/okiTdsKYqxavJPHbfUypcAACYXiYHLnbbjn0t/WoKDbKtLyZwmYvsNl2XLimbcGWxNFr10j7glcOQvnztOfofVy1NyEVLJXnucCuYYx1D427b2Tx6UvmHt27UhYtKNOQL6Pd7WiRJX7hqqTbUFmldTaGcDkNtA1619I+E79/Ybe1vMRUuiNE/vmu1fvbB88YFkUvK8uR0GBoYCah1zO/bbNnTYs1bKstzq2GG3+2VsxC42K9zxTmZ9zqH9FFfMvUcF7ud2OqqgnGvO1eEApdXGrsnPTfxl6Odauz2KD/LKYdhtUltS8ExC2BqBC7AHPLU/jYFTGl9TaGW8AYsaq5Q4EKFCwAA0+vJ4Ct/7QqXfa39agqdAKln8PicdF691ZbopvWTt2S5dEmpyvLcynU79K/vXqv3hlorJcpUbcXsq/jPrS1SYY5L//FX6/TOtdbFUm9fWalbz7PWkeN2am1tkSRpVyikkayTaxIVLohdRX5WeKaVze10hH+nJmuFl2x2O7ELFpbImCH0tAOXQ+0DSavGsUOn8vzMe51D+rDbijWHLvAYa1+rPb+laNzn11YXqjjHpf4Rv/ac7ptwv5+9alW3vHdjbXi+F1UuQHohcAHmCNM09USonZj9hg3RocIFAIDIdGdwhcvKqgI5DKuqYX+LdbKjjgqXOekzly/W45+6ONw67Gw5bqd+8ZEL9Ognp94mHvZsiSNjhpD7g6b2nrFOoG2oswIht9Oh//2OFXr0kxfpnneuGney+YLFpZJGQxpfIBg+cccFVki05ZP8zs6W1071Spq5nZgkLS3Pk9tpVeNMVjmQCOFZSaUcZ4hdXeiCjqaeqStc1lYXjPu802Ho0iXWc/+ju88oOCZU3Nncq12n++R2Grr1vNrwvCM7sASQHghcgDniUNugjnQMKstp6G0MTIsJFS4AAESmN4MDl1y3Mzy/YNAbkDTa8gNzi8vpCLcVm0ppXpbK8uKb1zKVySpcDrcPyOMLqiDbqaXloydyDcNQXXHuhCv7L1xstZbZ2WydjG7uGVbAlPLczvCMGiBRlk1RlTWVRFWXDPsC2hMKIi9cWDLj9i6nQ8vKrbUeSlJbscZuO9ikkgyxC1e49I6vcBn2BXQ0dJzZrU7Hun61dRHtE/vadPcfDsjrD0qSHghVt9ywpkoVBdm6IHS8vE6FC5BWCFyAOWLzPqu65c3LylVEn9mYjFa4BFO8EgAA0lsmz3CRpDVjWulkuxxxD0gHJmMHLkfHDCG3W4OtrymKaFbMhaGrnI90DGrQ6w/Pb1lUOjGcAeI1+js7NMOW0pP7W3XZfS/oz0c64n7cnaf75AuYWlCQpYURBuDJnuNiV7gwKwnxsC/oOLvC5WDbgAKmVJbnVlVh9oT7XbG0TP/7uhVyOgz994F2ff63u7XrdJ+eP9opSfrgBfWSpI11xXI6DJ3uG5kQ6gBIHQIXYA7wBYJ6an+bJNqJxYMKFwAAImO3FCvN1MClZjRwqSvO4cQ1kmJJeZ6chtQ77FfHoFfSmPktdUXT3TWsqihHtUXZCprSnjP9OtEVmt/CVfdIArsN3vGuIfkCU1+EZpqmfvzySQWCpp7a3x7349pX51+4aOb5LbYV4cAl8e3PPL6A2gasY5ZZSYhHfbH1+9PSNzyudfm+VisoXFtdOOXv/LvWVuvf/2qd8rOc2tHUq0//4g2Zkq5aVq4loQrJvCxneDbd6yd7k/iVAIgGgQswB2w93qUej09leW5duqQs1cvJWC5muAAAMCN/IKiBEasVV6ZWuKytGh+4AMmQ7XKE5z8cDs3EsFuDnVtbHPF+NoTCmV3NfaNX3TNXAklQXZit/CynAkFTjV1TXy2/e0z4t69l4lDvaL0WOlF8QQTtxGwrF1jhUDIqXE6GvraSXLeKM/R1DumhoiBLWU5DAdMKXWz7QjPkJmsnNtYli0v1w1s3qqowW4HQaYoPXVQ/bpvwHBfaigFpg8AFmAOe2GdVt1y/uiocGiB6TipcAACYUc+wX5JkSCrMcaV2MTFaVpGnbJf1Vqi+hKuXkTzLxrQVa+kbVtuAV05DWlsz/Um2sexwZufp3vBcCSpckAyGYUw6e+hsj+1pCX98um9E3UPemB9z0OsPhzaRzG+xLa8skCGpY9CrzsHYH38yJ8LBJscZ4uMIzeeSpObe6AMXyao8+8ltG3X1OeW67YI6nVs3PrC/cFGJJKtSLFFzlQDEh8AFmAMOtlov1m8+h+qWeFDhAgDAzOz5LcW57vDFCpnG5XRoRaXVjobABcl0TqVViXKkY1A7Q/NbViwoUK7bGfE+7PZje870M1cCSWe3FZsqcPH4AvrjAauNmB1c2+2RYvFGc58CplRbnKPaKCoO87KcWhQKRBJd5WLPSlrCcYYEqAvNcWnsGtLTB9r0sZ/v0MlQeB5J4CJJlQXZ+pd3r9UXr1424bb1NUVyOw21DXjD+wWQWgQuwBzgDdWW5mdl5lWm6YIZLgAAzKxnyApcSnIz+++Oz71piW5Ys0A3rFmQ6qVgDgtXC7QPhue3bKiNbH6LbVlFvvKznBr0BtQbqjBbxJX3SJKxVVmT2XKoXUO+gOpLcvSW5RWSpH1n+mN+vNdO9kgabYsUjZXhOS4JDlyYlYQEsluXfuPZo/rqEwe050y/3E5DH75oYUJas+a4nVpfY72uvE5bMSAtELgAc4A90NDtzMyrTNOFkwoXAABmZFe4ZOr8FtsFC0v0f65fpYLszA6OkN7GDiHf0RSa31IX3Yllp8MIn0yTpKrC7KgqZIBoLA8FLvbcobM9tqdVkrRpbXV4WPe+1tgDF/sEcTTzW2x24HIo4RUuVuCyiFlJSAC7IjFoSmV5bn36ssV6/FOX6I43NyTsMex2fK+G5iEBSC3eXQBzgB0QZDnJUOPhcljfPwIXAACmNlcCF2A21BTlKM/t1JAvEG7RdG6UFS6SVRXzcmO3JOZKILnsCpfW/hH1D/vHzeo62e3RjqZeGZLeubZK7QMjkqS9Z/plmqYMI7oLAPuGfeHqlGjmt9iSUeESNM0xrfs41hC/TWur1NY/oiVleXrbykpluRJ/3ubCRSX6z5catb2pJ6ZjEUBicXYWmAPsChdXhvZRTxdOWooBADCjbgIXIGIOw9CyitGr5GuKsrWgMDvq/WyoGw1pmCuBZCrMcakq9Dt6dluxzXtbJEmXLilVVWG2llcWyOkw1O3xqaV/JOrH2nOmX0HTapEXy3FhBy6neoY1MOKP+v6Taesf0bA/KKfDUH0UM2WAqeS4nfrcmxr0zrVVSQlbJGltdaGyXQ51Dfl0rHMoKY8BIHIELkCGM01TvtAMFzcVLnFx0VIMAIAZ9RK4AFGxKwak6Oe32NbVFMq+toqr7pFs4dlDYwKXQNDU5r1WO7Eb11VLkrJdDq0Itc3bG8McF/vEsL2PaJXkubWgIEvS+BZor5/q0T1PH9KLx7sUNKN7b2e3E6svzpGL99fIEFkuR7h60p6LBCB1ePUAMlwgaMr+E5IZLvGhwgUAgJnRUgyIzvLKsYFL9IPBJSk/yxU+mWbPzQCSxZ49NDZwebmxW+0DXhXnuPTmZeXhz6+x57i0RB+4HO+09t9QHnvV1tltxTbvbdHnfrNbv9/dojsf2aP3/+Q1/fqN0xryBiLaX2OXFbgsppIMGeaiRSWSpOeOdqZ2IQCY4QJkOt+YcIAKl/hQ4QIAwMwIXIDojK1wObcutgoXSbrnnat1qsejtTWx7wOIhF3h8t8H2rS/1Qoy2kItw65bvWBcW6Q11YX67c4z2htT4GJVuDSUx1bhIlmBy1+OdelA24D+c+sJ/eClk5KsarKjHYNq7Pbon7cc0XdfOKEvXbNU71pbPe3+TnaH5rcwKwkZ5h2rF+g7L5zQayd7dLp3WLW0xANShsAFyHD2/BZJcjPDJS5UuAAAMLPuoVDgkkfgAkRi5YICFee4lJ/lDJ/IjsWCwtjmvwDRWl9bKKchDYwExlWuOA3ppg0147a1K1wOtA4oEDTD76lmYppmuKVYIipcntrXqlCnbX304oX67JVL5PEFtHlPq36xo1lNPcO65+nDWliSq3Prpq40O9EVClxo3YcMU1OUo4sWlWjbyR5t3tuiT1++JNVLAuYtAhcgw9nzWwwp4j9uMbnRCpfgDFsCADB/UeECRKcg26WHPnyBnA6Dv9eREeqKc/Xzj1yg073D4z5fXZQzITRsKMtTrtuhIV9AJ7qGxlV0Tad9wKtBb0BOQ1pUEnu4sbLKClwCphUI/d1bl+uvQqFQfpZLt5xfp/edV6v/74kDevpgu/7X5v164EPnqzw/a9L9hVuKldJSDJnnxnXV2nayR4/vadUnL1ssh8FrDpAK9B8CMpxd4eJ2GjJ4MY0LFS4AAEzPNE31DvslSSW5XLsFRKqqMFsVU5zgBdLR0vJ8Xbm0fNy/ySq0nA5Dq6qin+NitxOrL8kd16IsWtWF2Wooz1N+llPfeM+6cNgylsMw9NW3r1BDWZ7aBrz6/544MOl7vmFfQC2h1mlLmOGCDHTVOeUqzHappX9Er57sSfVygHmLwAXIcPa8Eea3xI8ZLgAATG/YH9SI37rYozSXk8cAAGlNKHCJZo7Lsa7424lJkmEYeuCD52vzpy/RFQ1lU26Xl+XUvTeuVo7LoW0ne/TDlxonbHOy26puKc5x0TYTGSnH7dQ7VlVKkh7f05Li1QDzF2dogQznDVW4uGhPEDcXFS4AAEzLnt+S5TSU6+atBABAWlsTS4XLoCRpaZyBiyRluxwqyJ656nJpeb6++vYVkqQfvnRSzx1sG3d7YyhwWUQ7MWSwG9dXS5KePdyhvmFfilcTuaBpatgXSPUygITgXRKQ4ewZLlS4xM/psL6HVLgAADC5sfNbaGUKAJCkNdXWHJXD7YPy+iObh2m3FGsoj2zmS6Jct3qBbj63RqakL/7yjfDrmiSdCFXdLC6LfaYMkGqrFhRoeWW+vAFT/32gPdXLidjnfrNbN/3oVXUOelO9FCBunKEFMpx/zAwXxIcKFyDz/elguz7y0A419XhSvRRgTrJPTBXn0moFAGCpLcpRSa5b/qCpw+0DM25vmqaOdSampVgsvnT1Mp1Tka/uIZ++/+Joa7FGO3ApJXBB5jIMQ5vWWVUumdJWzOML6LWTPeoc9OqJva2pXg4QNwIXIMNR4ZI4zHABMt9je1q0r6Vfj2XImwsg09iBSymBCwAgxDCMcJVLJHNcuoZ86hv2y1Bqwo0sl0P/v7cskyQ9svO0jnZY7c3sGS5Lymgphsx2/aoFcjkM7W8d0KG2mUPQVLOPPcl6P2eanJNBZuMMLZDhfEEqXBLFSeACZDz7ZPBrJ3tSuxBgjhrbUgwAANva6sjnuNjtxOpKcpTjdiZ1XVO5cFGJ3rG2SgFT+uZzR2Waphq7rJO+iwlckOFK8ty66pxySdLjGVAxYleXSdYspV2n+1K4GiB+BC5AhgtXuDg4nONFSzEg8/WGTgbva+nXwIg/xasB5h4CFwDAZNZWF0mSXjvVO+MFbOF2YikONr56wxq5nYZeaezR73a3aMgXkNOQ6ktyUrouIBE2rbXaij19oC3tK0Yau8e3g358T/qHRMB0OEMLZDgfM1wShgoXIPP1eKyQJWBKO5p6U7waYO4hcAEATOaChcUqyXWrtX9EzxyaflD38U6rhVdDef5sLG1Ki8rzdNsF9ZKkf3v2qCSptjiHdt2YEy5YWCxDVgu/7tDfb+nKrnC5cmmZJOmPB9s15A2kcklAXHgVATKcXeHi4o/CuLlCoZU/FGIByCxef1BDvtE/zF+lrRiQcN1DocAlj8AFADAqx+3U+zfWSpIefK1p2ivqj4dOri4tT33rro9fulBleW6N+K33gLQTw1yR43aqttiq1rLb+KUru53fjeuqtbAkR0O+gLbMENwC6YwztECGC89wcVDhEi+nEWopRoELkJF6h8dfuUXgAiReLxUuAIApvG9jrbJdDu1vHdBrp3qm3M4++duQBoFLfpZLn7uyIfz/xaWpXxOQKPYxdiyNAxfTNHUy1FJsSVmeNq2zWqE9vqcllcsC4kLgAmQ4u8Ily8XhHC8qXIDM1htqJ5Ydej480jGoriFvKpcEzDl2276SXFeKVwIASDcleW7dGDpZ+sCrTZNu0zPkU1eoWnJJmlSTvGtdlVYtKJAkraxKbZszIJHsKrJ0rnBpH/COm5/0zjVVchjSjua+cKsxINNwhhbIcHY44KLCJW6jFS6UuMxnfzzYrmcPd6R6GYiBPVuitihHyyutN8uvUeUCJJR9nJXmZqV4JQCAdHTbBXVyGNJLJ7p1uH1gwu12O7GaomzlZTlne3mTchiG/v3mdfo/16/U21cuSPVygIRpCAcugyleydQau63nhLqSXLmdDi0ozNZlS6xZLpv3tqZyaUDMCFyADGdXuDDYL36jFS4ELvNVx8CIvrp5v772xH75qHTKOPaJ4OJcly5aVCJJ07azABCdoGmGW/dR4QIAmEx9Sa7esrxS0uRVLvaJ33RoJzZWWV6WblhTJScXMmIOaSi3LkJL55Zi9vyWRaW54c9tWlclSXpiX6v8Qc7PIPNwhhbIcN7QSWG3kz8M40WFC3af6ZcpyRsw1TfsT/VyEKXRE8HucODCHBcgcfqG/bLf8xYzwwUAMIUPXVQvSXr6YLta+obH3Waf+G0oo3UXkGxLyqwQo2vIF744Ld00hua3jJ2f9Kal5SrOcal9wKubf7RNN//4Vd3841f13h+/qkd2nk7VUoGIEbgAGc5O+90ODud4uUJVQlS4zF97zvSHPz57ADvSX7jCJcetjXXFchpSU8+wzvQOz3BPAJGwj7H8LCeVtQCAKa2pLtSFC4sVCJp6eHvzuNvsWRJL06zCBZiL8rNcqi7MliSdSNMqlxOhNoOLy0YrXLJcDr1nQ40k6XTfiE52e3Sy26PGbo/+a9uplKwTiAbvlIAMZ7c9clHhEjdXqMLFlNU2BfPPnjN94Y/7PFS4ZJre0M+sONetgmyX1lQXSaLKBUiUntCQ49I8qlsAANP70EULJUm/23VGR9pH50fYM1zSraUYMFfZx9qxNB1Af3KSwEWS/uaKJfqvvz5PP7jlXP3glnP1rZvXSZLO9I1oYIT36khvBC5AhrNnuGRxpWncxoZWVLnMP/6gqf2tVLhkMvvqe3u2xEWLiiURuACJMnqMEbgAAKZ32ZJSrasplMcX1Cd/8YZeaexW/7Bf7QNeSQQuwGyxj7XjaVjhMuwL6EzfiKTxLcUkyeUwtLa6UBvri7WxvliXLilTZUGWJOlox+CEfQHphDO0QIazAxdmuMRv7IBE5rjMP8c6BuXxBcP/72WGS8axQzJ7tsRFi0olWYGLyTENxI3ABQAQKcMwdN971um8+mINegP6wiN79N0XT0iSFhRkqSDbldoFAvPE0nDgkn4hRVPPsExJhdkulUVQQX1OhTX76QiBC9IcgQuQ4fxBu6UYh3O8XA4qXOazPS394/7fm6ZDBTG1nlBLMftk8PraImW7HOoY9Opo+0AqlwbMCeE5SQQuAIAIFOe6df/N6/WOVZUKBE39+g1r2DXVLcDsaSi3Qop0rHBp7B5tJ2YYM19EHA5c2glckN44QwtkOG9ohovbQYVLvMZVuAQJXOabvWPmt0hSHxUuGSd8MjjHumIy2+XQhlprjsvWo50pWxcwV3TbFS45BC4AgMhkuRz6vzes0scvWRj+nH0CGEDyNZRZAWfbgDftZp+csOe3lObOsKXlnErruYOWYkh3BC5AhhttKcbhHC+HYcjOXOzKIcwfu89YFS6rqwokMcMlE/VO0u7ookUlkqSXCFyAuHUNWcdYeT6BCwAgcg7D0GevbND/944VWltdqHeuWZDqJQHzRmGOKzz7JN2qXBq7PJKkxWWRVb0tC7cUG6JlNNIaZ2iBDOezK1yY4ZIQdpWLnwoXSVLHwIh+9HKjXmnsVnAO/0EzMOLXidAfn1c0lEmiwiXT+AJBDXoDksa3O1pTVShJOtTaP+n9AESuY9AadFyen5XilQAAMtGN66r1X399nlaF/j4DMDvsKpe0C1y6Q4FLhBUuDWV5chpS/4hfrf0jyVwaEBemlAEZzg4GqHBJDJfDkC9gEriE/PTVJv1ie7MkqaYoW+9aW6V3ra1WbXFOileWWHtb+mVKqi3O0ZLQH6PMcMksvaGAzGFYQxdti8usP95Pdg3JHwjK6eC5EohVJ4ELAABAxmkoz9O2kz06lkaBi2maarRbikVY4ZLlcmhRWZ6Odw7paMeQqovm1nkJzB2cdQAyXLilGDNcEsKucGGGi6Wpx7rixJB0pm9EP3jppN79w2367osnUrquRNsbaie2rrpQRbnWyfpeKlwyij2/pSjHPW4e04LCbGW7HPIFTDX3DqdqecCcYAcuFQQuAAAAGWNpeajCpSt9Zp90Dvk06A3IYUgLSyKrcJGkc8JtxdLnawHOlrDA5Z577tHKlSv161//OqLtX375ZX3xi1/UVVddpXXr1umSSy7Rxz72MT355JNxrcM0TT366KP64Ac/qAsuuEAbNmzQ29/+dv3DP/yDzpw5E9e+gXQ02lKM/DQRXKGr36lwsXQMWCfX7t20WvfcsErn1RdLkv6wtzWVy0q43Wf6JEnraotUHBoGTYVLZrF/XsU544t3HYYRLlG3S9YBRM/rD4ZbLVLhAgAAkDkayq2QIhEtxbqHvPrJKyfDrWZjZVe31BTlKMsV+fksAhdkgoScod2yZYseeuihiLYNBoP6+te/ro985CP6wx/+oJaWFvl8PvX09Gjr1q2688479eUvf1nBGAZWB4NB/e3f/q2+/OUv69VXX9XAwIBGRkbU2Nion/3sZ9q0aZNefvnlqPcLpDNmuCQWFS7jtQ1YfVHrSnL1jtUL9I/vXBX+vD8Q/fN0OjJNc3yFS+iEPTNcMosduJTkThzmbZeo23/UA4he55D1ptrlMCYEmwAAAEhfDaEKlzN9IxoKzb2M1Q9eOqnvvHBCX3xkj0b8sZ8TGG0nFnl1iySdUxkKXNoJXJC+4g5cnn32Wd15550RByTf/OY39fDDD0uSrrnmGv3yl7/USy+9pF//+td605veJEl69NFH9dOf/jTqtXzjG9/QE088IUn62Mc+pqeeekpbt27Vfffdp+rqavX39+uOO+5QS0tL1PsG0pXdUsxFhUtCuEKBSyorXEzT1MPbm/X0gTaZKRxU7w8E1T1kncSuLLCuZi7Lz5LbaShoSq0Dc2NIXXPvsLo9PrmdhlYuKAifsB/2B+P6AxKzy24pVjxJ4LIk9Ed8YxcVLkCsxs5vMQwu8gAAAMgUJbluleVZ75NOxHERmmmaeuFYpyTpQNuA/u3ZozHvy+4+sLg0svktNrvC5URoRieQjmI+QxsMBvWtb31Lt99+u7zeyMrIDh8+rB//+MeSpBtvvFHf+c53tHHjRpWVlWnDhg36/ve/rw0bNkiSfvSjHykQiDx1bWlpCYc0n/rUp/SVr3xFDQ0NKi8v1/XXX6+HHnpIxcXF6uvr0/333x/lVwukL1+QGS6JlA4VLnvO9Ovfnj2qrz5xQF9/6qA8vviuQIlVx6BXpqwQyg4hHIahmtBgupa+uRG42NUtKyoLlOVyKD/LKbtgrG+YtmKZwp65U5I78cr7cIVLNxUuQKzsFpPMbwEAAMg8dpVLPG3FTnR5dKZvRE6HIUPSI7vO6A/7Yms3bl8MtyTKCpeaomzlZznlD5o6QctopKmYApcXXnhBN910k+6//34Fg0GtXbs2ovs9/PDD8vv9Ki0t1d133y2HY/zDO51OfeQjH5EkDQ4OqrGxMeI1PfDAA/L5fMrLy9NnPvOZCbfX19frwx/+sCRp8+bNGhripAvmBrulWBYVLgmRDhUuLxzvCn/8h/8/e/8dJtdd3o3/7zO97Wzvq7JqlqzmJndsXAg2YDAYHtMhtBRQQvhBDCHxN08ggeQh3/AEU75JCAk2CSbBxJXqCraRiyzJkmVJVtf2MrvT+/n9cc7nzOzu9D1n2r5f18WFvDtz5uxO2ZnP/Xnf9yuT+PAPXlrWLpRKTWUtrpmydjP3e+0AgNEmGUCuzW/pbwEASJIErzbHhW3FGoWWcHHkaCnWzoQL0XKJlmKc30JERETUeIbVTWgnllFweVpdq9i1qg0fv2INAOArvzxW0TwVsRlObI4rlSRJWKfOpDnOtmJUpypaof3oRz+KI0eOwGq1Yvfu3fj6179e9DqyLOOnP/0pAOD2229HS0tLzsvdfPPNePnll/HSSy9h3bp1JZ/TU089BQDYtWsXPB5PzsvccMMNAIBIJIJnnnmm5GMT1bOk1lKMCRc91EPC5ZkTypuYd+7sR6fbhhMzYXzonpfwqyNTVT2PKbVlWLfHvuDrIuEy5m+OgsuhcXV+S79X+5qY4zLPhEvDKDTDZbW6a2o2nGBqiahCTLgQERERNa5htUhxcqbyIsUzasHliuF2fOTy1bh8TTuiyTQ+/8ArCMVL36wYT6a1DZxic1w5Noo5LhUUeoiqoaKCiyRJuPHGG3H//ffjU5/61JKkSi7nzp3D7KzyxLz88ssXfC+ZzDwpzWYzbLbyPsjF43GcOHECAAqmbTZt2gSLRVlEO3jwYFm3QVSvEur8JCsTLrrIJFxq0wt0OhjDq5NBAMDHr1yDez5wES5e1YpwIoUvPnwY5+aqt0NfJFx6Wha+JmcKLo3fUiyeTOOI+vsWCRcgMwdEtKmi+jcXES3FlhZc3DYLetVkFlMuRJXJJFyWPseIiIiIqL6tEy3FKuyeEYon8dK5eQDAVcMdMJsk/NWbzkOPx4bTvgj+/rHS57mcnYsgLQNum7mi9PT6LhZcqL5VtEL705/+FN/85jexfv36kq9z7Ngx7d9r1qzB3Nwc/u7v/g433HADtm/fjm3btuE973mPNvS+HBMTE1rRZnBwMO/lzGYzent7AQAjIyNl3w5RPYonRcGFCRc9WLSES21u/5mTPgDA+X0t6HDZ0OW24a537sBwpwtpeXkD7so1mWc3c3+rsnDdDAmXo1NBJFIy2pxWDLY6tK+LhIs/Ylwa4r4DY3j82LRhx19pRBqpNccMFwBY16WkXznHhagyMyHlOcaECxEREVHjETNcRuaiiFYwJ/aFM3NIpmUMtjqwWk2ltLts+H9uOg8A8NixachyaZ1CRtR0y1CbE5JU/lrWhm7lZ3mNLcWoTuVelShieHi47OtMTWVa4UxMTGD37t0LvpZOp7F3717s3bsXTzzxBL761a/CbDaXdGyfz6f9u7W1teBlRbsxv99f8HIVPN+bmvh98PdSf8SsEZvZxPtHB1rCRU5X9fcpbkv0RL1quF37mtUsodtjw8mZMIKxZNXOazqkJFh6WuwLbnNALUyMzUcb/jH36oSSbtna1wKTKfPDtGUlXIz4GUfno/jKL4/BJAH/+t4LFrQzo8qIGS5tLuuS+0ySgHXdbjx7YgZnfJGGf9wS1cJ0SE24eGwLnkN8j0hkHD6/iIzD5xetNJ1uK1odFsxHkzjti2Bzb+5xDPloaxXrOhZ8dr5oVSvMEhCKpzAdii9YP8j3/JoMKGsNfV57Rc9B0VJsPBBDKJ6Ex17R8jaRYar2iAyFMlXH3bt3w+/343Of+xxuueUWtLe348iRI/j617+O3/zmN3jggQcwMDCAP/mTPynp2PF4XPu33W4vcEnA4VAWCqPR/DuzOzrcMLM9U06dnbln71DtiIJLd5cHXV3l/cGkpRzqH2q324Guruo+3hOpNJ47PQcAePNFqxbcfpfaxku2Wqp2XnMxZdfLun7vgtvcalV+R5PBONra3bA08OvlWFhZpN++un3Bz9jbruyYSZgkQ37fJwLK7aZl4Cu/Oo4Hd18Nm6Vxf4/1QLR/W9vflvO1cH238rWxYKLqz22iZiCKmhsG23M+h/gekcg4fH4RGYfPL1pJ1nZ7sP/sHIJyeZ9zZVnGHnWt4uadg0uuu7bLjeNTIcwkZZyf9b18z6/5pLKONdzTUtFnsy4A/a0OjM1HMZ2QsXaQz2OqL1UruEQimZ7p09PT+Jd/+RdcffXV2te2b9+Of/qnf8InPvEJ/OY3v8F3v/tdvPe979VagBWSPUOmWBRNxNsKzZ2ZnQ1xl8MikqS8UM7MBJAvITgdjCGcSGvRQqoO0VIs5I9gWqrdoPdmIau9xGbnw5ieDlTtdiUJODYfRyCWRLvTigGHacHt29XXpNHpYNXOa0RtveSU5QW3aUrLsJgkJNMyXj09gz6vI98h6t4r5+YAAH0uy4Kf0QbluTQ+a8zj4MTonPbvIxMB/J+HD+H3rlqr++2sFMlUGgExbycaX3KfiYQLABwd91f1uU3UDGRZ1nYiWhKJBc+hUt4jElFl+PwiMg6fX7QSdTiUTkInxuYx3ecu+XqvTYUwOh+F3WLCxlbbks9Tq9ucOD4Vwr4TM9jS7ij6/Do1qXQd8lqkij+bDXc4MTYfxQuvTWGthzMGqXpKKRJWreDidGYW4V//+tcvKLYIZrMZn/nMZ/Cb3/wGiUQCjz32GN7znvcUPbbL5dL+HYsVHuIsvl8sCcM/uLnJcu7fjSzL+Mh/7sd0MIaf/8EVjPNVUUJNuFhMEh+3OjCLlmIpueq/z8ePTAIArhhuh4SF96fHpjynAtFU1c5rOmuGS/ZtmiQJfV47zs1FMTIfRW9L4xZcTswoRaW1Ha4FP6NXfQ2bjyQM+X2LWQhtTivmIgl8b89ZXL+xGxu6S3/TSxlzEaXYIgHw2C057zORcDk3F0EyJWvPdSIqbi6S1BK17U5bnveCfP9MZBQ+v4iMw+cXrSTdbmUtdDIQK+tx/4zaTuyioVbYLeYl1x3udOHxY8CJmdCC7+V7fk34lbXZ3hZ7xc+/DV1uPHPSh2NTIT6Hqe5UrX+J251ZRLr88svzXu7888/XCijHjx8v6dgtLZnKUiBQuDIqvt/e3l7Ssak0E4EYRuejiKdkTAXjxa9AukjLMlLqAoi1QGqLSmdRf49iYamannhVmWt11XDHku+1qEPcg7FkVc4lFE8iFFdainV7lhao+9VUy7i/cJG7ns1HEphVW4qt7ViYzGvNmuFihNmw8jp5w6YuXLO+E8m0jC/94mhNHnfNYD6q3I9ehyVvIWWgzQmbWUI8JWPMn7+tKBEtJea3tDosbH9IRERE1KC6PTYAwFSwvM/xmVmzS9cqAGBdh7KOe1Ld0FjMeCBTcKnU+i5lnfn4dKjIJYmqr2qfmIaGhrR/F0qXSJKkDbYvNGclW39/vzabZXR0NO/lUqkUJiYmAAADAwMlHZtKc3Qq8wIXSaRqeCYrSyKVWZy1mLlbWw9isTZV5YXvcX8URyYCMEnAZWuWFoRFaixQpYLLVEBZXHPbzHDZzEu+3+9VXsdH5xt34frUrPJmsLfFDrdtYSrPqxa4/OpCvt5EwaXDZcXnb9wAj92MV8YD+OHeEUNur9mJ2RKiUJaL2SRhldry8vRsJO/liGipGbXg0um21fhMiIiIiKhSYjPlZBkbpYOxJPaNKC3ArsxTcBnuVAouJ2bC2iiHfFJpWbv95RRcNqrdIV6bDhW9TaJqq1rBZfPmzdq/z5w5k/dyyWQS8/PzAFDS/BZAmceyYcMGAMDhw4fzXu7IkSNIpZRiwJYtW0o6NpXm6GRQ+zcLLtWTUOeNAIC1gQeX1xOLaClW5YLLMyd9AIDtA96ci8Yt1S64hJQdJ2IHzGIi4dLISQHRTky8OczW5lATLhGDEi5qS7EOlw3dHjv++Jp1AIDvPH0Kz56aNeQ2m5loKdbqKNy7d027cl+f9pW284qIFCy4EBERETU+8fl+uoyCy3Nn5pBKy1jd7tQ2sC22ut0JkwT4o0mti0Q+M6E4UmkZZgnoytFNo1RrO1wwmyQEYymMNPBGUGpOVVuh7ezsxPbt2wEAv/jFL5BM5l7Eev7557U5KxdccEHJx7/22msBAHv27EE4nHsh5dFHHwUA2Gw2XHrppSUfm4o7xoRLTSSzEi5WJlx0UauES7GIrmgpFjCoxdViojVgrnZiADDQqqYKG7ilmEi4rMtRcPE6MwkXI3bLaAkXdfHybdv7cMXadsSSafzxjw/in585jTR36ZRsPiJm4hSeH7amgwkXokqIgksXCy5EREREDatHS7iU/jlezG+5Ym3+0QwOqxmD6hpBsbZiE2o7sS6PXdvwWgmr2YTt/cqIid+e8lV8HCIjVHVL/O233w4AOHfuHL7zne8s+X4sFsPXvvY1AEBfXx+uvPLKko99yy23wGw2w+/346677lry/bNnz+L73/8+AOC2226D1+ut5EegPI5OZSdc0gUuSXpKpJXftdkkwSSx4KKHWiRc4sk0njutvEHIF9GtdsJlUn0T1JMn4dKnthQbb4KEy9qOHAUXtcAVT8mIJvV/TZtRd/10upREhiRJ+D9v24p37OiHDOCfnj2NP77vIOaK7A4iRSktxYDMfc2EC1F5pplwISIiImp43S3Ke7lQPIVwvPhmaVmW8azYHLou91qFMNyptPg6UWLBpW8Z7cQEsX4iikJE9aKqBZfbbrsNu3btAgB84xvfwBe+8AUcOnQIPp8Pe/bswQc+8AEcPHgQAHDnnXfCbF44N+Cee+7BTTfdhJtuugkHDhxY8L3h4WG8973vBQB897vfxZ133onjx49jdnYWP/vZz/D+978ffr8fbW1t+MQnPlGFn3blCMaSODeXWXRlwqV6xAwX6zJ2BdBCtUi4vDQyj0gijZ4WO87rcee8jCi4BKtUcBGLa/kivgNqS7Fxf6zqaSC9nJrJn3BxWc1a8U2kJ/Q0GxIzXDKLl3aLCV94w0b85U3nwW4x4benfPjAPXtx1sc0RjGi9VtbkYILEy5ElWHChYiIiKjxuW0WuKzKWutUCSmX16ZDmAzGYbeYcNFQW8HLilbdJ2cKD7EfVwsuy5nfIoiCy/Nn5hAzYKMkUaUK997Qmclkwre//W188pOfxJ49e3DffffhvvvuW3AZs9mMP/3TP8UNN9yw5Po+nw8nT54EAEQiSxdLPve5z+Hs2bN44okncO+99+Lee+9d8H2Xy4XvfOc7GBgY0PGnouPTC19MIyVUyUkfYoYL57foJ5Nwqd4f6/0jytyqqzd2QZIk5Ook1WJX3hQFYknIsgzJ4ESTGGKXL+HS5bHDbJKQTMuYDsV1ebNUTaF4UnujlyvhIkkSvA4LZsMJzEeT6NMxFBmOp7TUTId7aYHgzVt7cV6PB3/6wCGcnYviP148hztu3KjfCTShuaiacHEUflsj7uvpUBzBWBIee1XfBhE1LCZciIiIiJpDt8eG074IpoJxrMnxWTibmDW7a3Ub7JbC605iI+PJ2dISLnqsIWzqdqPLbcN0KI595+ZxWYG2Z0TVVPVV2paWFvz7v/87vva1r+Hqq69Ge3s7bDYbhoaGcOutt+JHP/oRPvzhD1d0bLvdju985zv427/9W1x66aXwer2wWq0YHBzE7bffjgceeAAXXnihvj8Q4cjkooILEy5VoyVcOL9FN7VIuIypc1DWd3vyXqZFHQaelpX4r9HEbpd8M1wsJkl7g9SIbcVOqQmHDpc1bxsq8XV/VN+Ei5jfYreYtN1Fi23oduN9lwwByCx0Un6ZGS6FEy4euwUdahu3M0wOEZWMCRciIiKi5tDdUvocl6e1+S2F24kB2QmXwgUXsX4g2pQvhyRJuHJYKbI8zbZiVEd02do5NDSEI0eOlHx5SZJwyy234JZbbinrdnbv3o3du3cXPfatt96KW2+9taxjU+WOZc1vAYAIY3xVI2a4LGfQGC1Uixku4g3HYJsz72XsFhNsZgnxlFyVnflTasKlO0/CBQD6vXaMzkcx6o9i52Croeejt0LtxASRlhDtqvQymzW/pVBSSRQP5gxoadZsSi24AMCaDhdmw/M4NRvG+X0tRp8aUVNgwoWIiIioOYguFuIzfz7BWBIH1G4coqhRiOgmMBtOYC6cQFeey+mZcAGAq4Y78MDBCTxzchafuW69LsckWi72IaJlOzqlJFyG2pSZDlEmXKomk3DhU1kvtUi4iNZWAwUKLgC0IkvA4DkuaVnWFtfyJVwAoF+d4zI2X3xnTL0Rg/xytRMTWh0GJVzE/JYiC5csuJRO/I7ypZWyrWlX57gw4aKrMX8U//fJE/i9e/drBU1qDtFECsGY8t6OCRciIiKixtblVj7jF5vhsue0DylZ+fw0VGStAgCcVjMG1NRKobZiouDS1+Io9ZQLunRNO8wmCad9EZyb42c8qg9cpaVlSaZlbYbLjgFlyEGYM1yqRsxwsbHgohuLSfldVppw8YXj+IcnjuNEkUFxQlqWtTccA22F33C0VKng4gsnkErLkKCkMPIZEAWXBmwpJgb5DXe6817GKxIuUb0TLmrBxVV44bJVK7gYe383A3EflZpwAYAzRXoLU3GyLGPfuXnc8cAruPVfnsM9L5zD3nPzuO/AWK1PjXQ0o75m2cwSPPbcbRCJiIiIqDGUmnB5Rm3RddW64u3EBPH5Ot96SDyZ1jo+6JVw8dgt2KmuR4qZM0S1xlVaWpazvghiyTScVhM2qvMnOMOlepJqwsXCGS66EXPgKk24/PzVKfzHiyP4/vPnSrr8bDiBREqGSQJ6vYULLqIAENC5ALCY2OnS4bbBUqCYJ3quNmLB5dRsCS3F1MV7vVuKzahvMDsKFLOATPHAH01UNXHVaJJpGX71OdHqLN5qjwkX/fw/Pz2Cj9+7H48dm0ZaVtoMAsUHZVJjmQkpr1ldblvBNohEREREVP/EDJdCCRdZlrXixZUlzG8Ris1xEXNj7BZTSZ/dSnXlsHKOz3COC9UJY4cAUNMT81s2dHngsim7HqMJznCpFjHDhS3F9LPchItIeM0U2S0iiPkt3R570fuxWi3FJtVz7ykwvwUABlpFwqWxWopFEymMzCu/97UFCi6ZhEuNWoqpt5+Wlfu8lPTGShTIun+8juK/I9FG7uRMGP/8zGm8ZVuv1h4vLct46dw8Hjg4jsePTSOy6O/ZrtVt+Lu3nm/4DKVG8eRrMwCAN5/fg/dfsgqheBIf++H+ooMyqbFwfgsRERFR8+hW39NNFlizODoVwnQoDofFhAuHSp/XKgouJ/J8Hhj3Z+a36LmR56rhDtz165N44ewcookUHFamsqm2uGJAy3JkUokJbupxw2lVFovDTLhUTVzMcDFxx6lexAyXSgsucbXNW6lzN8QbDpEWKSTTUszY59i0uuukWK/+/qyWYmlZhqlBdj6f8UWQlpWCSqGWaa1awkTvlmLKY6PQbQOAxWxCi92CQCyJuUiCBZc8RMu1FrsFlhJeCwdaHVjT7sRpXwT/9Oxp/POzp7FrdRvO72vBL49MacW4XJ4/M4cvPHgY//D2rQXTXytBMpXW/t5/+vXr0ea0Yl593ZsIxBCMJVmYahIzLLgQERERNY1udWPldCie93O8SIrsWt0Gm6X0zz3riiRcRDt1vdqJCeu7XOjx2DAZjGPvuXkt8UJUK/wkTMsiEi6but1wWkTChQWXakmmRMKlMRa6G4FlmQWXhFoEK7ngog2MK6HgoiYegga3FNMSLkXOqafFDrOk/MyzoTi6PPq+aTKKaCc23OEquKumVSRcdB5aX+oMFwBoc6oFl3AC4HvGnMT901ZiJN1sknDPBy7CE6/N4IGD43j+zByeU/8HAG6bGb+zuRtv2dqHoay5SqdnI/ijH7+M35724auPvoYvvmHjim6vlJ20E8XgVqcVnW4bZkJxnJ4NY2u/t1anRzpiwoWIiIioeXS5bZCgtFH3hRM53+NVMr8FyHQTmArGc36Onihj/aMckiThyuEO/M/L43jm5CwLLlRzLLjQshydUhIuG7s92k7XxS1YyDgJbYbLyt5prSeRcKl0Zkai7ISLspu+r8j8FiCzqOk3uKXYVIkJF4tJQrfHjvFADKP+WMMUXES8uVA7MQBodRibcOlwF0+stDmtODsXLfnxtBLNaQWX0hNADqsZN23pwU1bejAyH8FDBydwajaCq9d14IZNXTkj6B0uG/76LVvwufsP4f6XxzHU6sCHL1ut28/RaMTzwm0za6+bgNJGYCYUx4kZFlyahUi4FPubQERERET1z2I2oUPdJDUVjC0puPijCRwY9QNA2YULj92iJU1emwxijXvhsvN4QFn/0DvhAmBBwYWo1rhKSxWbCcUxE4pDArCh2w2nukAVYcKlasTivo0FF91oCZfU8lqKRZPpktJeoqVYf1ktxYwuuIgZLsXPqV/McSnQhqneiITLuiIFF6NmuIjFy1ISLqKtGQsu+YnfTWuFLdcGW534vavW4iu3bMGbt/YW7Pd7zfpO/P+uWw8A+OZvTuEXr05WdJvNQLwOieeJsK6jcBsBajxsKUZERETUXMQcl6kcc1z2nJ5DWlY2UvWXsDF0sXWdbgDAa5OBJd8zqqUYoLQ/s5gknJ2L4icHxvDo0Sk8enQKjx+bhl/nz/RExTDhQhUT7cRWtTvhtJq1GS4suFRPQk1hsKWYfrSEi1xZwSW7UDMXSaCvyLC2clqKearUUky86epuKb64NuC14yUoc1wahUi4DBdLuKgL+PPRJGRZ1qV9VCyZRiiuvEZ2ltRSjAWXYubV50OlBZdy/a8LBzEyH8V/vDiCv/zZEWzubcHqdmdVbrueiIJLy6I5LeJ5dXKWBZdmwYQLERERUXPp9tjw6mSmu0W2p9WEyJVrK2vLNdzpwm9P+3BsIogbhtsXfK+cGbbl8tgtuGCoFS+cmcPf/PLYgu9ds74Tf3/rVt1vkygfbounih1T24lt6vYAABMuNSASLqUMiqbSZBIulbXGi2ddr5RF8kpaihmfcFHeBHW7i78JEuc95l/6Rq0eJVNpnPFFACgzXAoRM1xSaVkrkiyXmN9iNUvw2AsX44Dsgoux93kj0xIujurtIfmja9ZhW38LEikZL56dq9rt1pOAWuhqceQuuJxgwqVpcIYLERERUXMR81onFyVc0rKMZ7X5Le1LrlcK8Xng2GRwyfcyCZfykzOl+P0r1+CyNW24cKgVFw614vy+FgDAy2qLNKJqYcKFKnZEffHc1KPEBUXBJZpIIy3LMK3gYcLVIma4WNlSTDfLTbgkFiVcCokkUtru/FJ2eHirUHCJJdPaOXV7Skm4KG+URhsk4XJuLopUWobLai4aY3ZYzbBbTIgl0/BHk/DYl/8nczarnVgpiZl2UXBhBDqv+QpmuCyX2SRhc48HB8cCDZXu0pOY4bI44SJa9Y3NRxFNpAq2aKP6l5Zl7XWLCRciIiKi5iA+6y9OuBybCmE2nIDTasIFg60VHVtsbHxtUcElGEtqGxmNaCkGADsHW3HXO3do/x1JpHDNPz4NXyQBXziO9hK6TBDpgau0VLGjeRIuMpRFWzKeSLiwpZh+LOblzXBJLEi4FC6MiDitx24uaTFftBQLGNhSTLzhsltMS2Yz5NLfqrxRGm+QRecTapujNR3Okgoees9xmQkrx+lwlVYc0BIuYRZc8lnuDJdKDajzi0YbaH6RnvLNcGl32dDmtEIGcHo2UoMzq38PH5rA0ycaY5jnfCSBlAxIKP11i4iIiIjqm+hmsXiGy/4RJQmyc6C14o29a9UNWCNzEYTimbUL0U7d67DAZavOpiyn1YwBdXMrWx5TNbHgQhWJJlI4o75YiYSLw5p5OLGtWHVoM1xMfCrrxSwtL+FSTkux8YDaTqzEOG01WoqJN1xd7tISGP1ZLcXkCn9n1XRyRikUrysyv0VodSgLjH6dWnrNltmap5UzXIoShc1qJlyAxmunp7dMwmXp711rKzYbquo5NYIxfxR/+bMj+LOHDiPdAK+Zop1Ym9MKC9O0RERERE1BzGtdXHA5MDoPANgx6K342G1Oq5aMFgUcILudmDHplnyGO5U1y5NseUxVxE9OVJETM2Gk5IUvpCZJgsOiPKRYcKmOpJZw4VNZL2JBqeKES7r0lmLlDoxrUWd+hOIppNLGLNSJhEtPCe3EAOXNkgQl1TbbACmM49PKmyzxpqsYvRMus2UnXJTbZ8ElP3HfiN9VtYidUo2S7tJbvoQLkClo8kPNUkcmlNYK4URKG0Zfz2Y4v4WIiIio6XR7RMJl4eaxTMKl8oILAFy3sQsA8ODBCe1rE+rnpuoXXPjZhKqPq7RUkRPqLvENXa4Fu+BFW7FIgi3FqiEzw4UtxfSy3IRLIllOwqW8HR7ZsxKCBqVctISLp7RzsppN2kLc4jdr9cYfTeDXx2cAANsHWkq6jkiYzOvUxm02nJnhUoo2JlyKEjNcRBqpWvrVlmJTwTjiK7CNpmhtmKsdoujbzA81Sx2bzqR+xhsgHTXN+S1ERERETUdssJyPJhFVN0xPBGIYD8RgkoCt/aV9Xs7nbdt7AQBPvjatfV6rXcJFTd/zswlVEQsuVBGxiLJ4l7hTbSsWiTPhUg2c4aK/Zc9wKSvhouzwEG25ip+bSXuOGdVWbFItmnSXmHABMgtx03W+W/v+l8cRTaaxvsuFC0scAKglXHQqeMyE1IRLiYuXouASiqcWzAciRTIta62tqj3Dpd1phd1igozMh4eVxF8g4cIPNfkdzyq4jDVAOkq8ZnW6Ob+FiIiIqFm02C2wqx1qxOf4A6NKumVjtwdu2/K6B5zX48GWfi/iKRk/f3UKQOYzU1+VCy5M31MtsOBCFckUXBbOQXDaRMKFBZdqyCRc+FTWi2W5CZdyZrj4y3/DIVIuRiVcptWES0+JCRcA6FKLM9PB+i24JNMy/mvfKADg3RcOljSfBsia4aJzwqWzxJZiLQ4LRD2VKZelpoIxyAAsJqnqA70lSUK/2lasERbO9SYSLi0FWoqdm4usyPRPIa9NNWbCpdNd3Q/GRERERGQcSZK0TZaiy8X+EWV+y3LbiYnj337JEADgwYPjALI6fJTYUl0va9X0/XQoDr9OrcKJiuEqLVXk5KxScFk8eJotxaorkVZ+zxYTEy56WW7CJV5BS7FSZ7gAmfY9RiVcpipIuHSq7bFmwvVbcPn18RmM+WNodVhw05aekq/XqvsMl/JaipkkSUtusOCy1Oi8SInZYSqxiKYnkU5bmQUX5fHozdFSrNNtQ4vdgrQMnPFFqn1qdSuaSOHsXOb30QiPm8wMFyZciIiIiJrJ4jkuIuGyc3D5BRcAeNsFg7CaJbw6GcSRyWDNWop57BathRpTLlQtLLhQ2aKJFEbmlEUCUSkWHFYmXKqJCRf9LXuGy4KWYvmLIqm0jEmt4FJaSzEgk3AJxIx5jok3Qd1lJFw6GyDh8sO9IwCAt+/o116nStGqDmLXL+EiWoqVvnjJgkt+IiFQals+vQ2oc1xGGyCpoDfxGpQr4SJJUlZbsdCS769UJ2fDyPoToRXd6xlnuBARERE1p253JuESjqdwdDIIANihQ8IFANrdNrx+QxcA4IGXx7NailX/s9s6dRwCCy5ULVylpbKd8UUgQ9n5vbiFi1PtAWlkweX4dAjHpoKGHb+RiPZVNs5w0U0m4VJZSmtxSzE5T+FmJhRHMi3DLJW3kCUWNwMGRGEPjvkx6o/BapaWtAsspN5nuByZDGLvuXmYJeCdFwyUdV2v2lIse4ZLWpbxzMnZstu6JVJprXBTasIFyMxxKVTAW6lGy5yDpDfRDnC8AZIKekrLsvb4b8mRcAEyLUeb6UPNkckg3vjtZ/Hj/aMVXV+0ExNz1+o94SLLMibUc+xkwYWIiIioqYhNlpPBGF4ZDyAlAz0eW1kbQot567ZeAMADB8eRSMmQAC1tUk3aZ5PZ5vlsQvWNBRcqm1g8WdvhWjIHwWXwDJdkWsYn7t2Pj/3nfkSZokFS3SprYcJFN2a1PVsyXekMl8z1UmkZwTxJFLGzuafFrt1mKYxMuIgUyO9s7tEW+UshFuLEcOV6c6/6c12/qbvs+LJIuMxnJVz+v6dP4Y/vO4gv/+JoWccS6RazSco5aDyfNiZc8hoTLcVaazNfQiRcxHmsFMFYEuKVLt9jeV0Tfqj5yYExzIYT+NWRqYqu/9q0UnC5eKgNgJLQyleUrwe/PDKFUX8MdosJ67vctT4dIiIiItJRT0sm4bJ/VJ3fMtiq621cuqYdPR4bomrr9S6PrSbrV5n0ffN8NqH6xlVaKptYPMm1A1606okaNMMlHE/CH00inEjV9byIahFpCisTLrqxLLvgsvCxn2+RXOyI7yuzANBi0AyXyUAMvzo6DQB4z4WDZV23Syu41F97HF84jp+/OgkAePdF5f1cwNKEy5GJIP79ubMAgMeOTmNkvvT5FGJ+S7vTWta8kTa16MOCy1Jjgdq2FBO3u9JaiomklsNiytvSstk+1MiyjKdPzALItF4s13G14HL1ug4AQCieMmwe13JFEyl846mTAIAPXbqqrCI8EREREdW/7Bku+0fU+S06tRMTzCYJb9nWp/13tee3COuaMH1P9Y0FFyqbeIHKVXBxqgWXsEHpk3A8c9x5ttdBXMxwMfGprBeRNklVWHCJqwUXcZx8i+QTFcxvAQCPups8qNNMEeHH+0eRSsu4cNCL83o9ZV1XDFOeDsXrbrf2fQfGEE/JOL+vBdv7W8q+fpsjU+BKpNL4q58fQUoGJAAygB+9lLu1kCzLS34Xs2oCaHErxmLaRcIlzILLYlrCpVYFFzXhMhWMVdyGsBGJIkGhpNawOuPtjC/SFL+bk7NhLZk4EagsmXJMbSm2tb9Fe16P1Wmx7gcvnsN4IIbeFjs+cMlQrU+HiIiIiHQmZrhMBmJ4eUwtuAzqW3ABgFu29mr/rlXBRcyfngjEym4NTlQJrtJS2QoVXFxW5SFlVLuv7EIOd3tn5oww4aIfi1q8qiThIsuy1lJMvHnJ9zgVi2x93vLecHjVhItfxzcJsWQa9x0YB1BZCqTLrfwM8VT+Fmq1kErL+PH+MQDAuy8aWNICsRQi4ZKWge88fQpHp0JodVjw52/cBEDpRZtdCBa++qvX8IZvPYszvkwCRqTyOsqchdDKlmI5pdKytgDeX+bzSC8dLitsZglpGZgI1ufCuRECasG3pUDBpbfFDpfVjFRaxtm5xm+5JtItgPJa5yvz+egLxzEbTkCCMrRTvPbX4/yfyUAM/7ZHSfL90TXDWnqZiIiIiJpHt9pSbNQfQzCWgtNqwobu8jZflmKozYmLVymtympVcGl1WrVW6KebqOUx1S8WXKgsyVQaZ+aUBUSxezWb+FBu1AyXBQkXA4aGNxqxuM8ZLvoRv8pKEi7ZRZpuT+GCy3Jbium5K+PnhycxF0mgr8WOazZ0lX19u8Wkndd0qHqt/gLRJJ49NZt3p/nBMT+mgnG02C24cVN3Rbdhs5jgVAvJ33/+HADgM9etx1u29mJ1uxPBWAoPvzKx4DpPHJvGfQfGMB9N4r/3ZRIws+rvprPMhAtnuOQ2FYwhlZZhNklaHL7aTJKkpdTG5mtXcDk0Hiirvd1yiYSLeN7nIklSZjjlTKgq52WkZ075Fvx3uW3FxPyWwTYHXDazlsoqNeHyyngA5+aqcx9/8zcnEU2msXPAizecV9lrJxERERHVt273ws9QW/u9Wot1vX362nW4el0H3r6j35Djl6LZWh5TfeMqLZXl7FwUqbQMl9WcszKttRSLGzXDJTvhwhhgIq38nm1MuOhmOQkXUQADMv1Q8xZc1MW63gpbigV0aikmyzJ++JIyVP5dFwxU/AYr01aseovOf/voMfzRjw/ioUMTOb//zEllR/oVa9vzzpkohUi5AMCVw+24eUsPTJKE/3XBAADg3r0jSKtFH380ga8++pp2+Z8dntSSaLNh0VKsvIQLCy65jYuUWItda+FXCwPaHJfaJBXO+iL46H/uwyf/62XtcWg0McOlUMEFaJ4PNaF4EvvOKYNERUvA8TJbgb02rfwONqjD50XCZayEx82YP4qP/uc+fOQ/9uVM1Onp4Jgfj7yizL36zHXrK0oGEhEREVH9s1lMC+b06T2/Jdvm3hb8w9u3aa29amFdB+e4UPWw4EJlES9MaztdOT+Ei53g0aTxCRcuPmYW+DnDRT/LmeESz5pTUCzhMlFhK6QWu1LU1GvQ8t5z8zg2FYLdYsLbtvcVv0IeXWo8dyZUnedlIpXGr48rBZWfHZ7MeZmnTyo70q9SB1RXqlUtcrmsZnzhxo3aa99btvXCbTPjtC+C36q73//vkycwE4pjTbsTHS4rfJGEdh6zFbYUY8ElN1HgqFU7MaHWraGeO+NDKi1jZD6KY5PVSZKIgm+hGS5A9YZTyrKMhw9NGJbyef70HJJpGavaHLhwSGmHUHbCZSoIAFivFlxEwqWUws0LZ5Tb90USeODgeFm3Ww5ZlvH/Pn4cAPCWrb04v6/8uVdERERE1DjEugVgzPyWeqKl79lSjKqAq7RUlhNqW5Bc81uATMLFsJZiWced5+IjZ7gYQCQ8ZJRfdBH3h0mC1h801yJ5KJ7UdoiX28PUa1cW3/UquPxwr5JuefP5vdqskEqIn7daLcX2jcxrrwcvnp1b8nueCsZwZDIICcDla9uXdVvi9e6Prh3W2kcBgNtmwVu3KUWqH+4dwZ7TPjxwcAISgL944ybcvEUZDihajs1oCZfKWorNR5MVDepuVmNawaW8lJjeBlpFwqU2LcX2jfi1fz99crbAJfUjZki1OAo/lqv1oebJ12bwlz87gi/9/Kghxxe/1yuHO7TX7EoTLhu7RcGl9ITLgdHMffwfL56rKIFZiqeOz+LlsQBcVjM+efVaQ26DiIiIiOqHKLhIALb3r4yCS6On76kxsOBCZRG7VHPNbwGyCy7GtxRjwUUZ3AtwhouesltqlVtwEfeH1WzKGnS+tDAiFuq8DgvctsI7xBfzONSEiw4txSYCMTx1fAYAcPtFA8s6llZwCVan4PL0icw8hZQMPPXazILvP6umSs7vaym7hddif3rDBnzvvRfgtp1Lf0f/68IBSACePeXDX/70CAClNdvOwVa8ZatScPn18RnMRRJZM1zKO592tUATS6YNe21tRGL2RX9rbQsu2iyO+dokXESrKyDTRs9oWsKlSEsx0TLgjC9iaLuzI5NKemTfiF/3lluyLGu/1+yCSzkJl7Qs44Q6w2W91lKs9ITL/qyCy5g/hkePTJV82+W4+/mzAIB3XjCArhrNRSIiIiKi6hGt0Dd0u+Ep8t6+0Yn0/dh81LBN4kQCV2mpLGKXatGEi0E9xrNfFOd0mmHRyBJMuOguu+BS7i7ieNb90V6gDZQ2v6XMdAuQmZkQTaa1+79Sp2fDSMtKAXVdp3tZx9JaioWrU3ARC6Cb1N3ijx2bXvB9sSP9quHltRMDlBku2/Ls9hlqc+J16zsBKOmevhY7Pvm6YQDKm9bzejxIpmX84tWpzAwXd3kJF4fFBLtF+XPNtmIZosBR65Zi/TVsKTbuj2I8EIN42Xp5zF+VzQgiYecp0lKs3+uA1SwhlkyX3YKrHKd9SiuxVFrG/tH5Ipcuz/HpMCaDcdgtJlw01Iq+CgouI3NRRJNp2C0mrGpzAsg8bnyRBKIFPvDNRxLaZpfbL1SKvt9//qzuabf9I/PYP+qH1SzhPcsswBMRERFRY1jTrrw3vXhVW21PpAraXTa0Oa2QoayFEBmJBRcqWSot4/SssqixLl/BxWZsS7EQEy4LiIIAZ7jox7yMhEtSTbjYzKaCczcmltEKKTsRE1xmWzFx/Vbn8neydHmq11JsZD6Ck7NhmCUlfQIAe077tJ8nmUpjz2kl4XLlMue3lOLdWYuTX/ydjXCpr4MA8GY15fLAwXHtNavcxI0kSdocGRZcMuqlpZi4/YlAzLBWT/mIdmKbe1uwrtOFtAztsW+kUhMuZpOEIbXAcMrADzXZH5heODNX0nXmIgn8xSOv4tlThVNBorh7yao2OKxm9Gr3d+kFtmNqumW4w6X9jWmxW+BWXysKpVxeHlPu49XtTnzsijVwWEw4OhXCcyX+nKW654VzAIA3belluoWIiIhohbht5wD+983n4eNXrK71qVQF24pRtXCVlko24osglkzDZpbyLnA5rcpDyrAZLlkFFy48MuFiBPOChEt5CZJMwqVwwUW0QuqrIOFiNknw2JVFOv8yU17aLnUdosOiTdZMFVqKPaO2C9sx2Iqdg61Y2+FEMi3j1yeUtmL7R/0IxVNod1qxpddj+PlcsqoNn3rdMD5/4wZcvnZhgeemzd0wmyQcmQxChjLfp62CWTmFHk8rUVqWtaTYQI1binV5bLCYJKRkZXaQ3n51ZApf+eWxnCmIfSNKmuOCQa+W5qpGW7HMDJfirx1i15zYsKG3tCxrCRcAeOFsaQmXH7xwDj87PIm/ePhV+KP5n1eZ+S3KLCiRTJwOxbW5XcUcn1IKLhu6M0lCSZIy82AKFG/E/JadA160Oa1423ZlbpRo/6WHU7NhPKm2ZXzfJUO6HZeIiIiI6pvLZsabzu+Ft8hsxmYhNo+fZMGFDMaCC5Xs2GQAALAma4fmYpkZLgYVXBILEy4reYC0LMtIZM0MIX2YJElrz1NuwiW7ANampkb80eSSXe9iobivwlZIoq3Y8hMuyvNJj4KLSLhUo6XYM4vahV2/sQsA8NhRpa3Y0yeU718x3A6TZHwxUpIkfOjSVTlnvLS7bAvamrU5rXlfPwthwWWhmVAciZQMs5TpO1wrJkkqawB6OZKpNL76q2O478AYHjo0seT7mYJLK67UCi4+Q+elAEBALVAUS7gAynsGwLjY/mQghlgyrb1uvzoRKPramEzLePgV5fc5H03in589k/NywVhSm58ifr8dLiusZglpGZgqMdH3mppw2dC1sHWjNv+nQMJlv5pi2jmotDV878VDMEvAntNzODIRLOn2i/nBC+cgA7hmfWfelrFERERERI1OzKNmwYWMxlVaKtkxdSiteIHKxWlRCi7xlFz2YnUpshMu8ZS8ogdIp2RA/IaZcNGXmONSbnug7AJYi8MKca8s3j0tZj1UMsMFyBRIAsssuIjrt+hRcFFnuPijScSSxj0vo4kUnldb6WgFl03dAJTB9eF4Stf5LXoQbcWA8tuJCSy4LDSqzm/pabEvmLtUK9rC+by+CZcXz81jXk2yiQKBMB9J4Pi08kFh56AXOwe9cNvM8EUSeFWnhfh8RLqulITL2g414eIzJuEikjOr251Y1eZAWgb2niucctlzyocpdS4LAPzXvlGcyvGh67nTPqTSMla3O7XWaCZJQo9a5JsoYeA9kL/g0ldk/k8ylcahcWWzy46BVgBKouvG85TXvLtfWH7KZToU1x5bH2C6hYiIiIiamNhcdJIzXMhgLLhQyV4TBZcCux+dWbMLjEi5LD7mfIE2IM0ue2A6Ey76MldYcNFaipkkWEwSvDnmbsiyrO2mWK222ilXi1ZwWd5zLKgVXMxFLlnaOdnUwt+sgSmXvefmEUum0eOxYX2X8lq0qduNwVYHYsk07jswhhMzYZgk4LI17YadRzmuHu7QZrB0uCqLare7WHDJJmZe1Hp+iyDOY1TnhMvjx6a1fx8cCywoCohWU2vanehw2WA1m7BrdRuATBssI8iynPXaUUpLMWMTLqd9Ye12LlF//hfPzhW8zgMHxwEA79jRj9et60AqLeMfnjy+4DLJVBr3q5dbXLwVxfKJQPGCSzSRwlm12LS+u7yEy5HJIGLJNFodFqzpyPy9+MAlqwAo7ebyFWtK9aOXRpBIydje79VSNEREREREzUi0FDs3FzF0oygRV2mpZCLhsq5AwcVmzrRjytVvfrlC8YXHXMmLjwsKLnWww7uZWEzKS2OlCRebumtapBJ84czjdDIYx3w0CbNJwnCne+lBSiAKOYFlFhz1nOEiSRI61ZTLtIFzXLR2Yus6IKntwiRJ0tqK/fMzpwEA2/u9aK1gVooRbBYT3ri5B4CSyKhEKxMuC4jCRn+N57cI/a1qS7F5/QouqbSsFVzEc+uhrJSL1k5sqFX7migMPGtgwSWcSEF9qdNeiwoRhYLJYHxBSlUvIuGypsOJS1a1AQBeKDBQ3heO46njyrySt27rw6dfvx4Wk4RnTvq0QlUwlsSnf3IQz5z0wSQBb9zSs+AYWjKlhILLiZkwZADtTis6FxVc+4skXEQ7s+0D3gXtEc/r9eDCQS9SMrTZK5UIxZP4731jAIAP7BrSXlOJiIiIiJpRp9sGh8WEtKy0JiYyCgsuVBJZlnFcLbisLVBwkSRJm+MSNqDdV2TRYs38Cl58FIv7ACqaCUH5id9nxTNc1OuLgkv24/SoeB51OLV2NuXy6JRwCUT1aykGZNqKTZc416BcsizjNydytwu7fpNScBFznq5aVx/txIRPXLkG77t4CB/ataqi62daii2vjVyzELNSBiqcg6Q3Lamg45v2/aPzmA0n0GK34E+uXQcA+OkrE9rr0j51tscFWamEK9TnxcGxAObCxvx9FK8bVrNU0muY12FFu/r4PePTP+WSnXC5WC24HJsK5S1O/vTwJJJpGVt6PdjQ7cbqdiduv3AQAPD1J45jZD6Cj/9wP/acnoPDYsLX3rYVW/taFhyjnITLq+pr/vpu95KCRl+RhItIMe0cWJo8ed36TgCVpZmSaRmPHZ3C7v9+GYFYEqvbnbhGPR4RERERUbOSJCkzf9agdQsigAUXKtFkMI5gTNmVv6qtcBskUXAxoqWYWEwVM0tW8uKjWNy3mSXuStVZxTNc0mrBxbww4ZK98HdsSunlv6nbU/H5teg0w0VrC1TCLvVSiF34Rr1xOeOLYGQ+CotJwq7VC9uFnd/Xgh5PZj7KlXUyv0VodVrx6devK1iwLoQzXBYSs1L66qylmJ4Jl8eOKumWazZ04rqNXfA6LJgMxvHCmTlEEym8os72uGAwk3DpbbFjY7cbMoDfnvbpdi7Z/FmF2lL/9oiUi0ij6OlUVsKl023DcKcLMnLPcZFlGQ8eVFJCb93Wp339Y1esRrvTilOzEdz+by/itekQOt02/NO7d2qFjWx9JRZcZFnGT/YrCZJLVrUu+b5IuEwFY0v+3siyjP1qUW3n4NLrite4vefmS04UB2NJ3PPCObzju8/hjgcP4+WxAMwmCX90zTpu3CAiIiKiFcHojaJEAAsuVKKTM8oi8ao2R9F5IU6r8n0jW4qJxa2VvPgoFmc4v0V/FSdckrlbimUXBo9OKbudN3ZX1k4MyBRcgsstuKjPJ49N34KLUW9cxE7ui4Za4bItnDtjkiRcp7YV63LbsGkZv9961OZcOg9oJcskXOql4JJpMVXu60YuaTnTTuz6jV2wWUx4gzoo/aFXJnBoPIBkWkaX24bBRW3VrlirLMQbNcdFFHpLaScmaHNcdE64RBIpreghbkO0FXsxR1uxwxNBvDYdgj2rzR+gpAZ//+q1AIBYMo3hThe+994LsKW3ZckxAKC3RfmdF5uf8vyZObw6GYTdYsJtOwaWfL/TbYPVLCElK0WXbKP+KKZDcVhMErb0Li3Qr+t0obfFjlgyjRfPLi0u5fLZ+w/h/z55AmP+GFodFnzkslV44GOX4toNTLcQERER0cpg9EZRIoAFFyrRyRllB2kpMyccBiZcREsxscjGlmKZNAbpp9KES1xNHYnr55q7oSVceipPuHi0GS7LK7iI63t0SrgYvVMke35LLu+8YAADXjvef0nzzSLQincGtYlqJLIsa7MzxOyUWuv22GE2SUil5bIe/4lUGj8/PLnkb9kr4wFMBuNwWc24bI2S5nrL1l4AwOPHprXnwgWDrUse61etUy7/21M+XYo/i1XSilAkXE7pnHA5ow6jb3VY0KbOR7lkdRsA4IWzc0su/8DBcQDA6zd0Lkn2vW1bH962rQ9vPr8H3333BdrGjlxKbSl29wvntGO3uZbOlDJJknassUXFG5Fu2dzr0d5XZZMkSWut+EwJxbVkWtZalH32uvV46BOX4Q+uHq54rhQRERERUSNiwoWqgQUXKsmkuvNyQ1fxdjhGzXBJy7LWUkwssq3k3d5icZ8JF/1VnHBRL2/TWootTCWE4kmcVRcIl5PA8KoLnf5lJlzETnW9ZrgYuVPEF45rLYKuXJu74LK2w4X7P34Z3nfJkO63X2tiBsZ8NIG0rP8ieiOZCScQS6ZhkoBeT30sFptNktZmqpy2Yt/bcwZ//sir+IP/OoBQPPN8Fu3Erl7Xoc1J2drXgrUdTsSSafxw7wiAhfNbhB39XrhtZsxFEnhtOlTxz5SPv4JWhGs61ITLrL4JF3E8cXxAScBJUIbVZ78WRRMp/PzVSQAL24kJZpOEP3/jJvzlzZuL/mx9aqJpPprMm+Y9OhnEb0/5YJKA914yWOBYIi2zsHgjiiM7csxvEa4cVoprT5+chVzkdWFkLoJESobdYsK7LhzIWcQhIiIiImp2TLhQNXCllkpy6/Y+fPTqYbzzgqUtMRZzqR/i9W4pFs0q4Iidp/PL3OHfyJJawaW5dvLXg0zCpbyiYSK58D5pV3c0+9SCy2tTIcgAuj02tLtsOY9RCo8OLcVSaVlr0ddi12fhrcvANy73HRhDIqUMuha75VcSkZZKy8tPNjU60cap22OHpY4KzqKt2Kg/imRaxlPHZ/DnDx/Gd397OuflY8k0/nufMt/j2FQIX3jwMJJpGbIs41HRTmxTl3Z5SZLw5vOVlEtcTTheMLR0tofFbMKA2mbMF9b/uVhRwqVdec6e8UV0LRieVgvY4viAkgbboBa0X8xKufz81UkEYyn0e+1aCqZSHrsFbrWt4XielItIt9ywqRuDrflfs/rzJFxEwSXX/BZh1+p2WEwSRuajWtonn5MzSnFquMMFU5MlAImIiIiISmV0K3QiANBnWzM1vTUdLvzFW3oxPR1AsbUShzrDRe+WYmF1969JyrTzWMkJF9FSjAkX/VWecFmYOhJtoES7IK2dWHfl7cQAoMWhLPQtZ+E9eze9R6eES5fHmDcuiVRmYfrdFw02XbuwUljNJrhtZoTiKcxFEloBZiUanRfzW+oj3SIoGwHm8cO9I/jHp04uKDxesqptycL5z1+dhC+SQIfLilA8hWdP+fB/Hn0N79jRj9H5KOwWkzYYXbj5/F586zenIANw28zY0JU7KSeKAaKoqie/NsOl9MfgYKsDZpOEaDKNyUBMS3UsV66ECwDsWt2GY1MhPHdmDjazCT98aUSbc3LL1j5dCg69LXacmAljIhDD2kW3P+aP4pdqmuYDuwon7sQGkrGshEswlsRr6t+LQgkXl82MC4da8fyZOTxzyrfk95DtpPq7Gu4snlQmIiIiImpWTLhQNXCllnSntRTTeaFHtChzWs1Zw8hXcMElzYSLUSqf4bK4pdjCx+nRqSAAYOMyB7qLneWBZSRcxHUdFpNuRbtONbUzG4rruov90aPTmA7F0em2aYPDVyK+7inEwnR/qz6L9noR53N4IoiZUBztTivOU2c1fes3pxa0fJJlWWsL9r6Lh/DlN22GBCXJ9eePHAYAXDncof09FXpb7Lh0TRsAZSHenGeGl8vAgouWcCmjpZjFbMKQ+vs5XSSJUY7Ts0sTLgBw8ao2AMD9L4/jcw+8ghfPzsMsAW/c3I33FymAlEqb4+JfmnD5zxdHkJKVeTJbelsKHke0JxvPSrgcGPVDhlKoEsnBfLQ5LicKz3E5McOCCxERERERZ7hQNbDgQroTCz1RnWe4iISL22bWdncvHjScz/6Rea09R7PQEi4mPo31Zqk04aK2FLOoRbDFC+RawqVnmQmXrIJLsb79+QSjajuxMhZNi+lw2yABSMn6FgXEwvRtO/tXdKIr83ha2S3FROslvVISerlxUzdWtztx9boO/N1bz8fDv3cZvva282E1S9h7bh7PnZ7TLrv33DyOTYVgt5jwtu19eP3GLvzJdesBZAbLX7+xK9fN4GOXr8FgqwPvKtDi021Tntd6b3wAKp/9tFbnOS6yLGtttBYnTC4aaoVNfR32Oiz40KWr8D8fuxRffvOWJUWsSmkFl0UtxfzRBP7nZSWR98ESijuLEy6vTYfwN788pv0cxYgU1N5zcwWTxaKl2DoWXIiIiIhoBRMJl7lIouw1H6JSsaUY6c5hURYz9G4pJnbqKgkX5aFbygwXXziOP/ivA7CYJDz2ySvrquf/cmRaijHhordKEy6JdO6ESySRRiSR0gouy064qEWSREpGLJmuaPhxUC1gemz6/RmwmCS0u6yYDScwHYyjYxlzaoSXR/04NB6A1SzhHTv6dTjLxpUpuKzsnTii4FJvLcWGO1348Ud2Lfhan9eBd+zox70vjeJbT5/CpWvaIEmSVkR88/m92gaC91w0iJG5CO59aRRWs4Sr13UsuQ1AmdvyPx+7tOC5ZBIu+hfnRMLFW2bBZU2HEzieSaUs11QwjnAiBbMEDLYtLL557Bb8423bMRmM4boNXYYMiNeSKYGFs1d+vH8MkUQaG7vduHxNe8nHmQjEsOeUD3c8+ApC8RTWtDvxiSvXFL3+2g4nBrx2jPpjeOHMHF63vnPJZVJpGae0lmLL+/tDRERERNTI2p1WmCRlPqovHEeXp74+V1JzaI6VZ6orToNmuIjjuWyZlmKxZBrRIrfz7CkfEikZkUQaQQN2+9ZKIiXSFHwa663iGS6phW3e3DazVrw5MOpXiiMWE1a1LW/ou8tqhugkFKywrZhYNNVrfoug9UPVaVi3WJj+nc092rFXqjYXEy4AMDavthSrs4RLPr972Wo4LCa8Mh7AU8dnMDofxVPHZwAAt1+0MKXyJ69fj09evRZ/dfPmZT03xQwXIxIu/gpaigHAmnY14eLTJ+EijjPY5syZfLt4VRtu3tJrSLEFyJ1wSaVl/Hi/km55/yVDJc2b6m2xQ4LyfuaP7nsZoXgKFw614rvvuaCkFJckSbhCtBU7mbut2Jg/ilgyDZtZwkCdteIjIiIiIqoms0lCu0vMcVnZ7brJOFypJd2Jdh16F1zEwpHLZobLmlnILta6KHsBwojFp1rRZrjk6eFPlbOobdrKTrhoBRfl+pIkacVB0U5oQ7c779yFUkmSlNVWrLLHtNYWyKHvYqQoikwHl19wmQzE8OixaQDAey4cXPbxGl2bgzNcZFnWEi6NUnDpdNtw+0XK4/c7T5/GvS+NIC0Dl61pw7pFaQOzScKHL1uNG5c5q8ht5AyXmPL485ZbcOlQCs16JVzEcVa3L6+AXam+FuXxl11wef6MDxOBGLwOC27YVNp9aDWb0OVRXjfTsjJn5q7btmvJp1JclVVwydVmUrQTW9Ph0t47ERERERGtVJzjQkZjwYV057SJgoveM1zUgovVvGAhe77Abu9UWsazp3xLjtEMMi3F+DTWm7nClmLx1MKWYkCmDdTzZ5TH4abu5c1vEcTucn+0ssX3SucwFKPnG5cf7x9FKi3jwkEvzuvV5/fWyEQrxZVccJmLJBBNpiEhkzBoBB+4ZAgeuxmvTYe01Na7LzKuiOhSWwUaUXDREi7lthRTEy7jgZguGzJEiyxx3GoTj79xf0wrcjxwcAIAcNPmHtgtpf9tPr+3BQDw4UtX4a/etBm2Mq4LAJesboPVLGHUH8tZ0BIFl+EOzm8hIiIiIup0K+s0Myy4kEG4Uku6M6qlWDirpRgAtJaw+HhwzK8tDgHG9LOvleSi9lWkn0pnuOS6T8Qi+asTQQDAph59+ueLxc6gmnCRZRmPH5vGa9Ohkq4vWpEZ1lJsmW9cYsk07jswDsDYhelGkpnhsnILLqPqYPFuj63sRelaanVa8f5LlAHqaVlJZYhh50ZwGdRSTJblrHRcea8dbS4rWtXriGH3y3FaPYZIzlRbj1pwiSbT8EeTmI8k8MRrSiLvrdv6yjrWX71pM3704UvwydcNw1RCG7LFnFYzLh5qAwA8naOt2AltfgsLLkREREREnS4mXMhYjbNaQQ2jGi3FgNIWHxf3M9f7nGqJCRfjVDrDJZ7jPhGPU3GkjTolXDxaS7EkAtEkPnv/K/jTB17BFx58paTri1ZkRiVclltwefrEDOYiCfS12HHNhi49Tq3hZVJ9K7fgIl7TG3EOxbsvGtTuw/91wUBFC+ul8mgFF303GcSSae1vTyWvHWvUhMXp2eXPcTmjHmNtjVIbdosJHepcpfFADD9/dRKJlIxN3e6yE3kum3nZxZArhtsB5J7jIhIu61hwISIiIiLSWvoy4UJG4Uot6S5TcDGqpZiyyKMtPhZoqfT0Sd+C/26qlmJpsbjPhIveKk24xHMkXLL78EsANnTpm3B58ewc3n/PXm0I98h8NGcP/8XqvaXY/lE/AOCqdR2cOaASr3m+FVpwOTkTxvf2nAEAvGNnf43PpnxumwV/99bz8fErVht+/i6DZriI1w2zlJkTU4416ryV08tMuEQTKYypaadaJVyATFuxiUBMaydWbrpFL2KOy0sj8wve68iyjJMzSvJxuFOfvz9ERERERI2MCRcyGgsupDvDEi5aSzHlYdtaZID0VDCGI5NBSAC2qLtNjehnXyvagHYTn8Z6qzThIlqK5ZrhAgCr2p3aQuhyiXY+//PyOEbnoxjwKgt/iVSm5U8hIa2lmD7nI+jVUuzQWAAAsL3fu+xzahYdWb/bUopqzSSVlvGlnx9FIiXjquEO3LS5p9anVJELh1rxiSvXGp5MNKrgIlp0euwWSBUkdPRKuJybi0KGUjBuL2O4vN5EweWp4zM4MhmE1SzhjVtq89hc3e7EYKsDiZSM58/MaV+fCMQQSaRhNklY1dZ4yTAiIiIiIr0x4UJG40ot6U7McIka1lJMJFyU/5+P5F5cflZNt5zf14JBtf1MUyVc1MV9CxMuutMSLqnyUlq5WoplLwZu6tZvd3F2MuV16zpw9wcu0nacz4aKJyACBs1w0SPhkkyl8eqkMvNma3+LLufVDHrUN4WRRHrBbKqV4L/2jeLlMT/cNjM+f+OGihb7VxK3+ndS94SL+rjzljm/RdASLjkGu5fjtE8p2KzpcNb0sSAKLg8dUtIt167vWlBkryZJkrS5QNltxU6o7cRWtzthYQtSIiIiIiImXMhw/ORFunOoCZewUQUXtaDTWmSGixgce9VwR2aAMGe4UAm0hEuZKYJEjpZi2Ytves1vAYBr13didbsTu183jK/duhVehzWTLgkXf9MgFk7LHXxdjDiHSCKNUIXzI45NhxBLpuF1WLC6vXbtguqNw2peMDNipRiZj+Cbvz4JANh9zTD6vNylX4zboBku/mUWakXC5YwvsqyU1ik1IbOmxq8PouAi0pBv3d5by9PR2oo9c3JW+/1yfgsRERER0ULZCZeV1j2CqoMrtaQ7l1EzXBKLEy75Cy7JVBp7TisJlyvXdWjXaa6Ei/JHwcaEi+4yCZdyCy5Li2DZBZdNPfolXC4YasWPP7ILH7x0lTZ8WyzGz4aLJ1yCBs1wcdnM2mvATAlJm1wOqu3Ezu9rMXSweCMSC7zj/pVRcJFlGX/zi2OIJtO4aKgVb9/ReLNbakEUXCKJdNmtEQsRrxuVJlyG2hwwS8rf81F/tOLzOKPOgBEFnFrJLv71eGy4dHV7Dc8GuHhVK2xmCeOBmJZsEQWX4Rr/roiIiIiI6oXYKBpNpptq9ADVDxZcSHdihksqLWs7/vUgiiXi+CLhMp+jtc7+UT9C8RTanVZs6fVoqZhmKrgk06KlGJ/Geqs04RKvYsIllw41FjtbQiw2EFOeC3q3FAMyu0WmQ5UVBQ6O+QEA2/rYTmwxscA7vozF6kbyk5fH8dyZOdgtJnzxdzaxAFcisckA0Heemmhl12KvrG2W1WzCNnUu06+Pzxa5dH6i4DhQ47STKIACwFu29Wl/O2rFYTXj4lVtADJtxUThZZgJFyIiIiIiAMq6otikxjkuZASu1JLuxAwXQN8Ch0i4iBfFNnWHba6Ey9MnlIWGK4fbYZIkbfEp1IwtxWq8wNOMLCblMVxuwkXMfLFlFcEGWh2wW0xY1ebQZnAYJZNwKfyGIS3LhiVcAKBTPY/lJly2DXh1O6dm0ScSLiugpdgLZ+bwfx59DQDwe1euYXu5MtjMkrb4r+eOreXOcAGA6zd1AQAeOzpV8TEmg8rjvyer4FELA97M7d+ytbbtxIQrF7UVOzkbAgCs69QvYUlERERE1Og6dZg/S5QPCy6kO4vZpO3w13NnrZZwsS1KuOQquJwUBRdl4UGb4dJECZdMmoJPY71VnnARRbDMfdLisOC/fvcS/Ot7LjR8uHOHNsOlcKEjHE9B/GRGJFw63coi5GQFRYH5SEJrF7SVCZcl+rwro6XYiZkQPvfAISTTMm7c1I33XTJU61NqKJIkwWPA3z0xw2U5s5+u36gUXPaN+DEdLP9xLMuy9trS02JsEbuYLo8dn71uPf78dzZiqK0+CoJijsu+ET9O+yIIxlIwSWDBkoiIiIgoizYDlwUXMgBXaskQou1XVMc5LqJ441aPLVo1RZNpRLMKO+P+KE7MhGGSgMvXKv3UjRogXEu5BrSTPizmSme4qPeJZeF90u91oM1VWQuecohkSbGWYiLdYjNLsFv0/zOwoVtpXbNvZL7s6x4aV9Itq9udC9qxkUK0FJsING9LselQHJ++7yCCsRR2DHjxlzefx1ZiFRAbDUI6/t0LRJVi7nKScX1eB7b1t0AG8PhrM2Vffz6S1Irb3e7aJlwA4PaLBvG27fUzW2hVuxOr2hxIpmX86KVRAMBQmxM2A17riYiIiIgaVaeLCRcyDj99kSEc6gf7SFKfnbWyLGttUcQikttm1pII2XNcnjnlAwBs7/fC61AWbMUQ73BcvwJQrSXVQcgWE5/GerNIlSVcRJs3W41SR9oMlyIJl4BacDEi3QJkdlg/d3oO8WR5zzkxv4XpltxES7GxJk24RBIpfOYnBzHmj2FVmwN//7athhQFV4JMwUXHlmLq7KflJFyATMqlkrZiE2oqpsNlZREhD5HufeDgOABgHee3EBEREREtIGbPVtoKnagQflIlQzit+rYyiadkpNQCg1hEkiRJ2wGfPcfl+dNzAIDL1rRnzkckXBLNlHARi/vc+a23ShMuos2bpUZzdURLsWIzXAIGzm8BgE09HnS6bQgnUnlTLt9++hS+9PMj2twbQZvf0s/5Lbn0qy3FpkPxsotZjeBLPz+KwxNBtDos+Po7tlclGdas3OrsMj1biomEi3eZrx1ijsvec/PwFXm9WkxrJ+apfbqlXomCS0x9jRhmwYWIiIiIaIHM7Nnm3MxItcWCCxlCFEX0aikWyVowEsUcAGhVd9mKgktalvHi2TkAwK7Vbdrl3E04wyXBGS6GMasJl2QZCRdZlrXUUa12XXeIlmLhBOQC5x6I6rNLPR+TJOFKtZ2fmKeU7dRsGP/62zN44OAEHjo0oX1dlmWtpdi2fiZccmlzWrXEx2QF8y/q2XwkgV8eURIPX3vbVs6cWCYjWorpMcMFAAZbndjS60FaBp4os62YeNz3tLDgks9FQ60LkmEsuBARERERLcSECxmJK7VkCIdaFIkk9ClwhNRkit1i0tqIAZk5LvNqweX4dAi+SAJOqwlbsxZsXc1YcFEX9znDRX+ZhEvpBcNEVhrGWqM2b2LoWyyZLthGKGhwSzEAuGqdssP66RNLCy6PvJIpsvzLb89oSY0zvgj80STsFhM2drsNO7dGJkkSetWF5vEmayuWPb/ngqHWGp9N4zNio0FAbd/p1aFYm2krNl3W9TIJF9uyz6FZOaxmXLKqTfvvdR18PSUiIiIiyibWTzjDhYzAggsZwmlVHlphnQouEXX2ilhAElq1lmLKItDzZ+YAABcMti5IfogZLnr2sq+1hLpIbWHCRXci4VJGvQWJdObCtSqCOa1m7blXaI6L0S3FAKWln1kCTvsiODcX0b6elmU88sokAMBskjARiOG+A2MAMu3EzuvxMLlVgJjjMh6I1vhM9MX5PfpyGzDDxR/V77Xj+k3dAIDnz/gWtAUtZiKofCBiwqUw0VZMArCmg2kxIiIiIqJsXW6RcGHBhfTHFS0yhChwRPVKuKgtUbLbiQFAm1NZ9JlX+8qLgkt2OzEg08s+mZa1VlyNTku41GheSDPTEi7pMhIuyayESw2LBR0udY5LgTcNwSoUXDx2C3YOKimFZ7Lair1wZg4TgRha7BZ8+tp1AIDv7TmDSCKlLbiznVhh/V4HgOZLuHB+j75c6t89vQouiVQaUbXQr8drx+p2JzZ2u5GSgaeOl95WTCRcellwKejaDZ3w2M24aFWrljomIiIiIiKFSLj4IomyupsQlYIFFzJEpqWYTjNc1MKNy7a44JJpKZZMy3jpnDKge3HBxZl1vWZJuWRmuLDgordMwqX0GS5x9f4wS1jQ9q7atIJLgUHUAa2lmLGLcFepO6yz57g8rLYT+53N3Xjnzn4MtTkwG07g3r0jXHAvUa9XJFyap+DC+T3607ulmHjdAPRrR3jDpvLbimVairHgUkhvix0/+eil+Prbt9X6VIiIiIiI6k6b0wqxnFaoQwhRJVhwIUM4dZ7hIhaMlrQUc4iWYgkcHg8gFE/B67BgU49nweUsJkkbINssc1ySKTHDhU9jvWUSLqUXXERLsVrfH51u5TkxU+ANQzVmuADAleoclxfPziOaSCEUT2oLq28+vxcWswkfv2INAOD7z5/DsekQAGA7F9wL0lqK+ZunpRjn9+gv01IsWeSSpRHtxDx2s25F5Rs2Km3F9pz2afNhCpFlGZNBteDChEtRbU4r0y1ERERERDmYJAkdnONCBuFKLRlCzJHQq+AiUilLW4qJhEsSL5ydAwBcvKoNJmnpYpBoc9YsBZd6WeBvRmIxsZyEi2gpVuv7QyRcfAUTLspzwMiWYgCwvtOF3hY7Ysk0Xjw7j0ePTiOaTGN1u1NLMbxxcw+GO10IxJJIpWV0um1sFVREM7YU4/we/bl0TrjMq3NWvDq+bqztdGFdpwvJtIxfnyjeViwYS2nJ2R6PTbfzICIiIiKilYdzXMgoXNUgQ+idcBHHWZJwUWe4zEUSeC7P/BbBpfNu31pLaAkXthTTm8WkvDRWlnCp7f3R4VKKkIUisYEqzHABAEmSFrQVe/iQ0k7sLVt7IalFUbNJwu9fuUa7zra+Fu17lFtfVksxWS79MVrPOL9Hf26dZ7i8eFZp2bm206XL8YQr1iqvES+P+otedkJNt7Q6LExuEBERERHRsnTmSbhMBWOc60LLwoILGcKp8wyXYgmXyWAMB0bU+S2r2nIeQ9vtq1MRqNa0GS4mPo31VknCJa4WwGy1TriUsEMjKFoDOYwtuADAlWrB5ZdHprD33DwkADdv6Vlwmes2dmGz2gZw5yDntxQjZlfEkmnMRZqj12xmfgvvf71kNhno8zfv0aNTADJtwPSyuVd57h+ZDBW9rDa/hSk4IiIiIiJaps4c6yd7Tvlwyz/twdceP16r06ImwJVaMoTWUkynhR6RcHHlmeEyG04gnpLR7bFhTYcz5zFESzG9zqnWRMLFwoSL7iym8me4iN0PtU64dNZRwgVQEmdWs6QVBi5Z3YY+tSWWIEkS/vat5+MPrlqLd14wYPg5NTqbxaS9MRwPNH5bsWgihaNTymI7Ey76cWstxZaf6jzri+DoVAhmCbhmQ+eyj5ftPLXYemwqWLTIrRVcPCy4EBERERHR8uRKuPzzs6eRkoFDattrokqw4EKGcNr0bSkmetAvLriIhItwyaq2vO2I9N7tW2uJOlngb0aVJVzqY6aOmOEyW2CGS7CKBReXzYyLhlq1/37L1t6clxtodeAjl69ekmKj3PpFW7EmmONyZDKoze/pY3JBN24dZ7iIdMslq9uW/N1drtXtTjgsJkSTaZz1RQpedjIoEi6c30JERERERMuzeIbL/pF57FdbHRdaUyEqRrfVti9/+cu4++678eUvfxnvete78l5udnYWV1xxRdHjtbW1Yc+ePWWdQzqdxsUXX4xwOFz0snv37oXb7S7r+FQ6p0XflmJawWXRYqzHboZZAtSwR975LYC+i0/1IJGujxZWzaiShEtcm6lTHy3FZkO5Ey6yLGsFF4+9OsWNK4c7sOf0HFxWM67b2FWV22x2fS12HBwLNEXC5WV15xDn9+jLpeMMl8eOTQMArt+kbzsxQClwb+x24+WxAI5MBgvOiJkMKB96mHAhIiIiIqLlWtxS7J4Xzmnfmw0nIMsyP6NSRXRZGXz00Ufxgx/8oKTLHjx4UI+bzOnkyZMlFVvIeGKXejSpU8IlT0sxSZLQmrXbtlDBpZlmuKRlWUtfcIaL/iwVJFxESzFbjRNHHWpLsXAihWiOx3okkdYKlNVIuADAm7b04pJVrfjDq9cywaKT3halLdu4P1rjM1m+Q2PKDiK2E9OXK2uTQVou/bVssZH5CA5PBGGSgNfr3E5M2NQj5rgEC15uIsgZLkREREREpI/shMup2TCefG1G+14yLWvt2InKtezVtscffxyf/vSnkU6XlmR45ZVXAACDg4N48MEH816ukgqiOLbVasXTTz8NiyX/j8d0i7Ec6gwXvdIkoTwtxQCg1WnFbDiBVW2OJbMhsum527fWkqnM4hlnuOgvk3ApPaFVLwkXt80Mm1lCPCVjJhzHYOvCmUbiDYPFJMFuqc65trms+Pb/2lmV21opmqmlmJZw6ffW+Eyai0f9eylDae/ptlX2lu+xo0q65aKhVq1lod7OK7HgIma49DLhQkREREREy9TpVjasTofiuOeFc5ABXLO+E3vPzSEYS2E2lIDXoW9LZVoZKi64pNNpfPOb38S3vvWtkostAHDo0CEAwPbt23Uveohjb968Ga2trUUuTUZy6TzDJaIVXJY+ZNscytd2rW4vfE5qESjSBAUXMS8EqP0CfzOqZIZLvczUkSQJHS4bxgMxzIYSeQsuLXYLo7ENrE8UXBq8pdhUMIaJQAwmCdjS56n16TQVu8UEkwSkZWXzQ8UFFwPbiQnZBZdCsf1JJlyIiIiIiEgnneqGsnhKxkOHJgAAH9w1hFOzYQRjEcxG4liL/C2PifKpaKX2N7/5DW699VbcddddSKfT2Lp1a8nXzS646M3IY1N5tJZies1wES3FrEsfsqIVyfVFZkNoCZcmaCm2IOFi4qK53iqZ4ZIpuNS+AKbNcckx5C0YVQsujuq0EyNj9DVJS7GDarplXae74oIA5SZJkvY7rTTZOe6P4uBYABKA6wxqJwYA67vcMEvAfDSJiTxFxFA8iWBM+Tl6WoxJ2hARERER0crhsJq12baptIzt/V7sHGzVWrXnm41LVExFK4Mf/ehHceTIEVitVuzevRtf//rXS7re/Pw8RkZGAOhfFJFlGYcPHzbk2FQ+hzWTcJGX0TteCBdIuPzJ69fjfz62C5etLZJwyepn3+gSaqrMLGXSGKQfcwUFl3ppKQZk5rjMhJe+ORAJF0+V5reQMXrVhMtsOIFYUp/Cdi0c1NqJcX6LEcTfvUoLLiLdcsGgF10GtvGyW0wY7lRSz0cmQzkvMxVQCshum5nFOSIiIiIi0kVnVtvkD+4aAgC0u/JvYiUqRUUrg5Ik4cYbb8T999+PT33qUzCVOLRbJFAkSYLdbsedd96J66+/Htu2bcPll1+OT3ziE3jiiScqOSWcOXMGgYCycNPZ2YmvfvWruOmmm7Bt2zbs2rULH/zgB3H//ffrsvhPxbmsmd7xeiwGagWXHAO3zSZpSdukXNxawaXxh14l6mhxvxlZ1Ne0SlqK2epgpo54wzAbypFwiYuWYhxe38haHRY41cRfvkSAkaaCMdz9/FnMR5a34+fgmB8ACy5GcS3z756Y32JkOzHhvB6l4HI0zxyXCbYTIyIiIiIinXV5lPWT1e1OXKOm+rWES45NrESlqGiL4E9/+lMMDw+XfT1RcDGZTHjf+96HZDKzAODz+fDkk0/iySefxDve8Q586UtfKjj0Pt+xAeCTn/wkEonMkyKRSGDPnj3Ys2cPHnroIXz961/XfX4MLeTIav0VSaS0xEultJZitsqPI4o1TZFwqaP2Vc2okoSLKIJZ6uA+6XDnf3MQiCqPfyZcGpskSehrceDkbBjj/ihWtxcvOuvp+8+fww/3jiCRkvGRy1dXdIxkWsbhCZFw8ep5eqTyLCPZORmIYf+oUhC7rkjLTj1s6vHg4VcmcSRPwWVSLSz2Gpi0ISIiIiKileX83ha8eHYeH718NUzqLMlOJlxomSpacauk2AJkiiKpVArDw8P41Kc+hYsvvhhWqxX79u3DN77xDbz66qu477774PF48MUvfrHsYwNAW1sbdu/ejauuugoulwuvvvoqvv3tb+O5557DU089hc997nP41re+VfB4nCW9kPh9lPp7MUsS7BYTYsk0osn0sn6fybSspWTcNnPFx3KpO/rDiVTD37+iEGA1Sw3/s9QjMfg+mZZL/v0m05mESyX3SbnPsUI6s2a4LD5eJuFi4WOnwfV57UrBJRCr+n05EVBmx5zxhUu+7d8cn8H/+8QJrWCclmVEEmm4bWYMd7oM/Rn0fH41kuyWYuX+7E8enwEA7Bjwos9rfJFjc68yj+3IZDDnuU5qCRfbirsf691KfX4RVQOfX0TG4fOLyDiN9Pz6g6vX4tYdfVjT4dK+Jjax+sKJhvgZqP5UdYtzPB6H2+3G2rVrcffddy9Imdx44424+uqr8aEPfQj79u3D3XffjXe+850477zzSjp2JBJBa2sr3G43fvSjH6G7O9P+4sorr8Rll12GP/7jP8Yvf/lLPProo3j88cdx3XXX5TxWR4cb5jrYpV6POjtLb/vitlsQS8ZhdzvQ1VV5u5jsljWrBlpht1SWchkIKwvN0aS8rPOpB2NRZcHSbjU3/M9SjxJW5aUxLZf+WLHYlD/IrZ7lPd7LeY7ls6ZXSQsE4ukl55JS26X1tLv42Glwa3ta8OwpH/w1eE0LxJXXoOlIsuTbvve+Qzjjiyz5+uvP60FvT3USLno8vxpJu8cBADDZLGU/Rk7PKwWO67b0VuXxdYXHAeAAxgMxmJ12tLttC77vTyobDdb2evnaVadW2vOLqJr4/CIyDp9fRMZplOfXQF/rgv9e26d8PvXHU/zsQRWpasFFpEqSyWTOdmEOhwN/8Rd/gdtuuw2yLOO+++7DF77whZKOfeedd+LOO+/Me2yz2Yw777wTTzzxBBKJBH784x/nLbjMzoZYwVxEkpQXypmZAEodg+NQUwJjU350LuORJuYTmE0S/L4QpArvnERYOU4wmsD0dKDyE6oDU7NKyxUT0PA/Sz3yq7NPEikZU1P+kh5z8+qO/2Q8WdF9UslzLB9rSmkfND4fWXIuk74wAMCcTvOx0+DabErx7MSEv+r35aRfTbjMhEq67WgihRdPzwIA/v7WrehSF9PNJmBDl9vw89fz+dVIrFB+2InZ0u6nbGenlb8zXotUtcfXYKsDI/NRPPvqOC5d077ge2emlPNpMfPvXr1Zqc8vomrg84vIOHx+ERmn0Z9flqSypjIxH+VnD1qilCJcTZr4F5rNsm3bNvT29mJiYgL79+/X9dg9PT3Yvn079u7dW/TYjfiCUA2yXPrvxpE1M2U5v89QTHmhU4beSxUfy2nNtFZp9Ps3nhQzXCr/fVB+pqwCSyqtLAoXE0/pc5+U8xzLpyOr3+jiYwViStLLY7PwsdPg+tTh4eP+WNXvS586H2gyEEMiJcNiKlyU3DfiRzwlo8djw+vWdSwpYlbr/PV4fjWS7JZi5f7ck0Gl8NztsVXtd3Zejwcj81G8OhHErtULCy5i80W3x76i7sNGstKeX0TVxOcXkXH4/CIyTqM+v9rVNRVfONGQ50+1V5d9swYGBgAAPp+voY5NC4kCRySRXtZxwgml4CKG3lfKbVOKccm0rBUsGlUyJWa41OVTuOFlLx6LeTnFJMR9Yqr9fdLhUtqbBWMpbf6REIiqM1wcy3s+Ue31agWXaFVvN5FKa4W7lAxMqbM1CnnhzBwAYNfqtopTilQ+UXAJx1NlX1fcr91VHFJ/Xk9mjstik2rBpbeK50NERERERCuPWFMJJ1KIJsr/LEVUk5VBuUh5MB5XdlU6nU7dj51IJCo+NpXHqS70RCpY6MkWVod8i+Mt93yATBGnUSXSIk1R+8X9ZpRdcEmVXHDJJFxqzeuwaD+DLxxf8L2g+nxssdck4Eg66vcq8zkmAjGkq7jtZi5rrhYAjM4XL/g8rxZcLlndZsAZUT5io0GwzL/DsWQa82pxtsdjK3Jp/YiCy9HJ0IKvRxOpzPm0VO98iIiIiIho5XHbzLCpazuz4USRSxMtVbXV2pdeegnXX389du7ciQceeCDv5VKpFE6dOgUAWLt2bUnHHhkZwRve8AZceOGF+Pa3v13wsq+99lpZx6bKOS3KwyuyzOJGWB3O7F5mwcVikmBXz6mS3b71JJOmqP3ifjNamHApLQ0lWorZLLUvgkmSpO3ImFn05iCY1VKMGluPxwYJQDwlay2+qmHxbY0VSdgEY0kcnlD63i5uE0XGqjThItItdoupqsXZ83rcAIDTvvCCnWRTanszR5XPh4iIiIiIVh5lTSXTqp2oXFVbGRwcHMTo6Cii0SieeuqpvJd77LHHEAopOxuvueaako7d29uLmZkZhMPhgsd+5ZVXcPz48bKOTZUTCz2PvzaNkJpSqUQ4oSZcltlSDMi0JWv8gkv9pCmakbmihItyOUsdtBQDsua4hBa+ORAtxTwOLlo2OovZhG41fbDndPXaZPoWJVzG5gu3FHvx7DzSMrC63am1QaPqcGsFl/L+Bk9nzW+pZgu4Lo8dHS4r0jJwbCqTcplUC0A9LXa2pCMiIiIiIsO1i02sISZcqHxVWxns6enBFVdcAQB45JFH8OKLLy65zNTUFL7yla8AAPr6+vDmN7+5pGNbLBa86U1vAqAkaR566KEllwmFQvjzP/9zAIDL5cK73/3uin4OKt0tW/tgNkl45qQPH7rnJRyfDhW/Ug6iJdlyEy5A9gDhygtA9UBb3GdLMUNIkgRRyyp9houacKmTIliHW3lzkL0bQ5ZlbfYGd4k3h5u29AAA/uaXx3BwzF+V2yw34fLC2TkAyvwWqi639jevvE0GkzWY3yLkmuMyEcgUXIiIiIiIiIzW6VY2Ny5u005Uiqqu1t5xxx2w2+1Ip9P4+Mc/jn/7t3/DqVOnMDU1hQcffBC33347RkZGYLFY8Nd//dew2xd+sL7nnntw00034aabbsKBAwcWfG/37t1oa2sDAHz+85/HP/7jP+LYsWOYnZ3FY489hne/+904dOgQAOCLX/wiuru7q/Izr2SXrW3HP92+Ez0eG077IvjwD17CTw9PlH0csVCkS8JF7PZt9BkuIuHClmKGESmXchMu9TJXJxN/zSyOx5JprYDEgktz+IOrh3HVcAdiyTQ+85NDODcXMfw2RcJF1BaLFVyeP6Okb1hwqT5XhQUX0cKr2139eSm5Ci6TasGlt4rzZIiIiIiIaOUSbdo5w4UqUdWVwc2bN+Mb3/gGPB4PQqEQvvKVr+CNb3wjrr76anz2s5/FyMgIXC4Xvva1r+Hqq69ecn2fz4eTJ0/i5MmTiEQWLir19vbin//5n9Hd3Y1EIoFvfvObeMtb3oIrrrgCf/AHf4CjR4/CarXiz/7sz/DOd76zWj/yirdjwIt7PnARLl3dhmgyjTsfOYJ7946UdQzR/sulQ8JF7PaNNHpLMXXRvB7mhTQr0Rqs5IRLur7avImCy0xWSzGRbjFLgNPKx04zsJgk/M1btuC8Hg98kQQ+fd9BzEeMfUModvis71LmbYz687cUmwnFcXw6DAC4eKjN0POipdzqrKZy22jWMuFyfl8LAOCRVybwqyNT6vkojzkmXIiIiIiIqBraOcOFlqHqK27XXnstHnnkEXzkIx/Bxo0b4XQ64XQ6sW7dOnz4wx/Gww8/jJtvvrmiY+/YsQMPPfQQdu/eja1bt8LlcsFut2P16tW4/fbb8ZOf/AQf+tCHdP6JqJh2lw3/eNt23H7hAADg4VfKS7mINIpLh4SLSMmUu9u33oiEi4UJF8OIhEupBZd4Ui2C1UnCpdO9dDdGMKY87j12C+cgNBGXzYx/ePtWLU34pw+8gngybdjtiZZiYmF8IhDLmwR7UW0ntqnbjTZ1hxBVT6VtNKe1Akf1EyWvW9+Ja9d3Ip6S8YWHDuPu589mWorVoABEREREREQrDxMutBy69JQZGhrCkSNHSr58b28v7rjjDtxxxx1l3c7u3buxe/fugpdpa2vDpz71KXzqU58q69hkLLNJwo2bunHvS6NlFzuMSLiUu9u33iTrrH1VM7KUWXBJagmX+rhPOnLsxtDmtzjYTqzZdHvs+L/v2I6P/XAf9p6bx5d/cRT/++bzDCmsiYLLxm4PzCYJqbSMqWAMfV7Hkss+d2YOALBrdbvu50HFZf/Nk2W55MfDVA0TLhaThL996/n4hyeO496XRvGPT53UCuBMuBARERERUTXkWlMhKlV9rAzSiuBRZ0YEouXttI0k9Cu4NMsMl3iqvtpXNaNyZ7jU232i7cYIZXZjiIKLx8aCSzPa0O3GV2/ZArME/PTwJP7pmdOG3I6Y4dLltqJPXQAfy9NW7AWt4NJmyLlQYaKlWFoGomWkniZrOMMFUF5/P3v9BvzJ69dBQuZ1uJcJFyIiIiIiqgImXGg5WHChqvHYlWJHsMzWJiIRo0dLMZe6+NTwLcXUxSeriU9ho5SbcEnUWeoo126MoFrs9DDh0rQuX9uBz9+4EQDwL789gwcPjut+G2KGS7vLhn6vKLhEl1xudD6KkfkozCYJFwx5dT8PKs5pNUGUgEv9uyfLcibhUoOWYtnee/EQvvrW82G3mOCwmDDYtjRFRUREREREpLcOdfPZbIgJFyofV92oakTCJZGSEUumYS9x4LshCZcGL7gkxQyXOklTNKNyEy5iro6tTu6TDnWGy3w0iWQqDYvZlGkpZudLfzO7dUc/Rv1RfG/PWfz1L4+ht8WOS9fo19JLJFzanVb0ex0A5nMWXJ4/4wMAbO1r0ZIWVF2SJMFlMyMUTyl/99zFr+OPJhFXC8jd7tonSq7f2IUtv3sJYom09j6CiIiIiIjISCLhkr2mQlQqPlqoalw2s7bTNhjLnXJJpNI4NxdZ8DU9Z7iIlEyjtxQTaYp6GdDejDIJl9La8IgFynr5I9zqsEL9EfDk8Rk8f8aHI5NBAECLffnPJapvv3/VWrxxczdSaRl/+sAreG06pMtx48k0gjHl9bPdZUV/q5I4GJtf2lLsebYTqwtijkuoxHTplNpOrNVhga3EjRFG6/c6sLbTVevTICIiIiKiFSJ7TWUuwrZiVJ76+CRNK4JJ3WkL5C+4/H/PnMbbv/s8fvHqpPa1kJ4FlyZJuCTqbF5IMyo34ZLUEi718bJqNkloV9uKff7Bw/jD/3oZ//Oy0l6Ku8Sbn0mScOcbz8OFg16E4il87v5DiOpQaBZvNM0S0OKw5G0pJssyXjg7D4AFl1or9+/epNpOjAPqiYiIiIhopTKbJLQ5lZTLDOe4UJnqY2WQVgyx0BvMs/AjduB/b89ZyLKy0K21FNNhhotbW3gqb45MvdFmuNTJ4n4zKneGS7wOi2C/e+kqrO9yYV1n5n/b+1vwpi29tT41qgKbxYS/e9tW9HhsODcXxXeePr3sY/rUN5ptLhtMkqS2FFtacDk5G8ZMKA67xYTt/ZzfUkvuMmeXafNbPLWd30JERERERFRLYjauL8w5LlQebnOmqvLYzZgI5E+4iK+/Nh3Ci2fnccnqtqyWYst/uGotxZok4SKKAqS/chMuoqVYPRXBbr9oELdfNFjr06AaanNa8Wdv2IRP/+Qg/nPvObzhvC5sXUYBxBdR3mi2qzt9BtSWYuOBGNKyDJOkPG+ePz0HALhg0Fs3balWKleFLcW6PUy4EBERERHRyiXmuMwy4UJl4ioIVZUY1h3KU3DxRzNf/+HeEciynCm4WJf/cM0sPDV6waX+FvebjcWk/G6TqeIFl7Qsa4UZWx0lXIgA4Kp1Hbh5Sw/SMvBXPz+KeLK0uUS5iDea7eobz26PHWZJeU2aCWV2/Yj5LZesaqv4tkgf7jJbimkFFzcTLkREREREtHKJz73Zn3WJSsHVWqoq0VIskKfgEsgquDx1fAbHZ8IQy926JFzUhaeIDrMMaokzXIwnNuWn5OIFl0RWUYZFMKpHn3n9erQ7rTgxE8b39pyp+DhihotIuFhMkjbrY3ReaSuWSst48dwcAGDXmvZlnDXpodyCi5jh0s0ZLkREREREtIJ1ukVLMSZcqDxcGaSqEgs/wdjShR9ZlrVCzPouF2QA//7cWQCABMChY8Kl0VuKJTnDxXCipVgpCRdRAAN4n1B9anNZ8bkbNgAAvvfcWRybClZ0HN+ihAuArDkuykL9q5NBBGMpeOxmbO7xLOe0SQdis0K+2WmLTasJlx7OcCEiIiIiohVMbDSc5QwXKhNXBqmqRMIl1wyXaDKtFRI+dvkaAMAvXp0EADitZm02wHKIGS6N31KMCRejiZZipSVcsgsuvE+oPt24qQvXru9EKi3jSz8/WvJ8omw5Cy6touCiJFxeUNuJXTzUphUuqXbK3WigJVzcTLgQEREREdHK1aEmXDjDhcrFggtVlVZwybHwI+a3mE0Srt/UhdXtToj1QLFgtFxudadvMi0va45BrWkzXEx8ChulnIRLXL2M2STpUhgkMoIkSbjjxg1w28w4PBHEofFA2cfwiZZirkz6oV9tPSUKLs+f8QEALlndtswzJj2IZGm+2WnZkqm0VlTrbmHChYiIiIiIVq5OFwsuVBmu1lJVebSWYksXfkQ7Ma/dApMk4fYLB7Tv6VVwcWYdp5HbijHhYjyLKLiUkXCx8f6gOtftsWNrXwsA4NRMuOzr+9QotYhWA1kJl/kY4sk09o34AQC7WHCpC9oMlxJml02H4pChvP61Zd3HREREREREK43o7MCWYlQuFlyoqgq1FAuoCZcWh3KZN2/t1RaKRCuw5bKYJNjVaeilLD7VK5FwsXBeiGHKm+HCmTrUONZ0uAAAp30VFFzUhEtHVkuxAXWGy6g/ipfH/Igl0+hwWbGu06XD2dJyiQ0LpbTSnFLnt3R7bEzrERERERHRitahFVwSkEvYjEskcHWQqqqUlmIt6mXcNgvetr1P+bddn4ILkCneNHTCJa0mXDgfwTAi4VLODBcWXKgRrGl3AgBOz0bKvq5oN5WdfujzKi3FJgIxPKfOb9m1ug0SF+zrgmilGYqVUnBR5rd0cX4LERERERGtcKKVdiota2uWRKWw1PoEaGURxZRgjhcqkXoRCRcA+PClqzAVjONN5/fodg4umxm+SAKheOO+WIpEhY0L/IbJJFyKz/phSzFqJGvVhMup2fISLrFkWktJdGTNcOltscMkKd//xauTANhOrJ64tJZixf/mTaoJlx7ObyEiIiIiohXObjHBYzcjGEthNpxAK9suU4m4WktV5VGTKsEcxQ5/bGHCBVCqyX/zli24el2nbufgKqOffb1KcoaL4bSES7p4wiXOlmLUQNZ0KAmXc/PRkgqKgpjfYjFJ2ms5oDzuuz1KIuLcXBQAcAkLLnVDzE4rJdUpEi7i/iQiIiIiIlrJxGZDznGhcnB1kKrKrc1wWbrwE4gqrWq8DmODV+4yFp/qVSLNGS5G0xIuJRRcEiyAUQPpabHDbjEhlZYxMh8t+Xpz6vyWdpd1SbuwAW9mgX6g1YHBVqc+J0vL5hItxcqZ4eJmwoWIiIiIiCh7jgtRqbhaS1UldtoGY8klA6cCahEmO+FiBFcTFFziSS7wG62chIto8WY18SWV6p9JkrBazHHxlT7HZTbH/Bahz+vQ/s12YvVF/M0LxVNFBz1qCRe2FCMiIiIiItISLj4mXKgMXB2kqvKoxZRkWkYsubCVjUi4GF5wsTZ+wUWkLrjAb5zKEi68P6gxrGlX5ricLmOOi0i4iB0+2fpbswouq9qWd3KkK5HqTKVlrf1hPtoMF7YUIyIiIiIiQrv6+XeGCRcqA1cHqapcNjNEJiO4qODhj6ozXAxuKdYMM1zYwsp4FrWYVdoMF+X+sFl4f1BjEHNc9Eq4ZLcU4/yW+iL+5gFAKMf8tGzToqUYCy5EREREREToFDNcQky4UOlYcKGqMkkS3PZMW7Fs4r+NbylWej/7eiTLcqaFFRMVhikn4cLEETWatR1KwuVMGQkXX1gkXJa2m9rQ7QEAbOn1oJPzP+qKSZLgtCqvTYWSncFYUtuI0O3hfUhERERERNThVjYc+phwoTIYu7JNlIPHZkEwlkJoUcHFH6tywqVBCy4pGRAlACZcjGMxlz7DhTN1qNGIhMup2dITLnMRZUdPe46WYlv7WvCPt23TCjlUX9w2CyKJOEKxzN+9YCwJp9WsFZen1HSLx26G02rOeRwiIiIiIqKVpF0kXDjDhcrA7dhUdaKgElhUcAmoLcW8Bhdc3NbGbimWTGVm3zDhYhyLVHrCJc7EK46+1QAAR3hJREFUETWY1e1KwcUXScAfLW2nTqGWYgBwxdoO9HsdOb9HtSU2GoQSyt/ZA6N+vOWf9uB3/+MlRNS/hZPBGAC2EyMiIiIiIhI61Q2Hs0y4UBm4OkhV57GJlmILCx6BKrUUczZ4wiWeXXAxMVFhlHISLsk0Ey7UWNw2i9Y26nSJKZe5iGgplrvgQvXLnfV3zxeO4wsPvoJQPIXDE0F89VfHIMtyZn4LW8IREREREREBYMKFKsOCC1WdWy2oZM9wSabSiCSURWujCy6ZhafCw4PrlZjfAmTmjJD+zFrCJV3kktktxfiSSo1jjZpyOe0rbY5LsYQL1S/xdy8QS+LOR45gMhhHb4sdJgl45JVJ/M/L45mESwsTLkREREREREBmw2EkkW7YjdtUfVwdpKrziIJL1gtVdnsxj8EFF5e1sRMuiVQmTSFJLLgYRSRcSmkpllAvY2PBhRrIGnXeSskJl7BIuDAB0WhcNuXv6vd+exa/Pe2D3WLC19++DX949TAA4GuPvYZnT/kAAD0e3r9ERERERESAsnlNbGCbCMRqfDbUKLg6SFWXaSmWKbL41fktbpvZ8NSG1su+QQsuSS7uV4VIuJTSUiy7CEbUKFZrCZfiBZdoIqXNvWpnS7GGIz4gnJxV0kxfuHEjNnS78YFdQ3jdug7EUzJeOjcPAOhyM+FCREREREQEAJIkoc+rfEYa80drfDbUKLhiS1XnydFSTCRcvA5j0y3Awl72jUi0FLOwnZihykm4xJPKZdhSjBrJWi3hUrylmJjfYjVL2msoNQ5X1n126/Y+vHlrLwDAJEn4y5vPw4A3U2RhwoWIiIiIiCij3+sAAIwz4UIl4uogVV2hlmJGtxMDAKe68BRJNGbBJa6mKSxc3DdUZoZL8YKLmPNiY8KFGsiaDiXhcnYuUjTJJea3tDutbGXYgDrdShHlvB4PPnv9hgXf8zqs+Opbz9cSekNtzqqfHxERERERUb3qU+dcjjPhQiUyfnWbaBGPXW3plZ1wiVYv4SJmuDRqS7GYOqDdYWHBxUgi4VJKSzEWwagR9bU4YDNLiKdkjPmj2kJ7IpXGPz97Glt6W3Ddxi4AgE9NuLRzfktDetfOATgsJtx8fi/sOf52bOltwTffuQNnfGFs6HbX4AyJiIiIiIjqk0i4jPmZcKHScHWQqq5FTbEEcsxwaalCwsWtDg9OpmXE1eJFI4kllUJRrkUz0o+YJVRKwkW0eeNcHWokZpOEVWKOy2xmjst/7x/D9/acxRcfPowTMyEAgC8cB6AkXKjxtLms+MCuVehy5y+YXTjUirdt76/iWREREREREdU/McOFCRcqFVcHqerc2gyXpS3FqlFwcWb1sm/EOS7RhJpwsXKOgpEsJuXlsZSES0JNuFjZUowazJp2dY6LT5njEkmk8G97zgBQColf/vkxpNIyfKKlmIsFFyIiIiIiIlo5mHChcrHgQlXnUQsewRwtxVqq0FLMYpK0dEgokSxy6fojWoox4WKschIucTXhYmXChRqMmOMiEi4/3DuC2XAC/V473DYzXh7z40f7RllwISIiIiIiohVJJFymgjEkU43XKYeqj6uDVHUeLeGS1VIsVr0ZLkBmjksk3ngvlFG1pRhnuBjLYip9hotIuNiYcKEGs7Yjk3AJRJO4+/lzAIDfv2otdl8zDAD41q9P4pWJAAC2FCMiIiIiIqKVpdNtg9UsIS0Dk8F4rU+HGgBXbKnqtIJLPAVZVhazg1VsKQYALjVlE4o3XsKFLcWqo7wZLqKlGF9SqbGsyZrhcs+L5xCIJTHc6cIbN/fg7Tv6cdFQK6LJNF48Ow+ACRciIiIiIiJaWUyShN4WJeUyxjkuVAKuDlLVeexKoSCVlrX2WP4qthQDMgWXcKLxZriwpVh1lJdwYUsxakxr1ITLdCiO/3ghk24xmySYJAlf/J1NC15r2l35h64TERERERERNaM+dY7LOOe4UAm4OkhV57Kaoa5la8kWbYZLlRIublFwiTdewYUtxarDoiVciredY0sxalQeuwUdamolmkxjS68H123o1L6/ut2J37tyjfbfbClGREREREREK00/Ey5UBq7YUtVJkgS3TSmsBGIp9f+rW3BxihkuDZhwYUux6ign4RJXEy4WJlyoAYmUC6CkWyRpYeHwPRcP4fK17RhodWB9l7vap0dERERERERUU/1MuFAZqrO6TbRIi92MQCyZSbio/+91VGf3tChWiOJFI2FLseqoZIYLEy7UiIY7XHjp3DwuGPTiirXtS75vMUn4x3dsA4AlxRgiIiIiIiKiZtfnZcKFSseCC9WE224BEEMwnkRalrXCS4u9OqkN0Y4rmmy8ggtbilVHeTNclMeR1cT7hBrPey4eRDSZwkcuW523oMJCCxEREREREa1UWsIlwIQLFceCC9WER20dFoylEI6nINa0W6qWcFELLmwpRnmUl3BRLmNlEYwa0NoOF/73zZtrfRpEREREREREdUkkXMb9UaRlGaYqbEqciyTw3GkfspelrGYJl69t10Y1UH3ivUM14VGH1gdjSfijSrrFbjFVrU2W3aLcfqwBEy5sKVYdFjWtUtoMF7YUIyIiIiIiIiJqRr0tdkhQZvjOhhPoctsMv82/+tkR/PrE7JKvv3FzN7785i2G3z5Vjiu2VBOZhEtSm98ivlYNbClGxVSUcGFLMSIiIiIiIiKipmI1m9DtUYos41Wa43J4IggA2N7fgktXt+GS1W0AgF8emcLIfKQq50CV4eog1YRWcImnEFATLt5qFlwauKWYSLiwpZixLGUVXNQZLhYmXIiIiIiIiIiImk2fmOPiN36OSziewnQoDgD4h7dvwzfftQPfftcOXL62HWkZ+I8XRgw/B6ocCy5UEx67UiwIxZLw1yThotx+QyZcEmwpVg3lJVzUggsTLkRERERERERETadfneMyVoWEy9k5JcHS6rCg1ZmZd/2BS4YAAPcfHMdcOGH4eVBluDpINeFRhzsFYkkERcLFwYRLKdhSrDpEwiWVliHL+YsuqbQMtaMYbGbeJ0REREREREREzaaaCZdzasFlVbtzwdd3rW7D5h4PYsk0/mv/qOHnQZXh6iDVhMchZriktIRLSzULLg2ccBEtxZhwMZZIuADQCiq5iHQLAFjMbClGRERERERERNRsqplwOeNTCy5tCwsukiThA7uUlMuPXhptyI3kKwFXbKkmPDal4BGMJRGIKhG4lhrMcIk14AuTaCnGGS7GsmQXXAq0FctuOcaECxERERERERFR89ESLgHjEy5n8xRcAOD6Td0Y8NoxF0ngoUMThp8LlY+rg1QTYl5LMJZEIKYUPZhwKQ1bilVHdsElmc7/OIlnJVysTLgQERERERERETWdaiZc8rUUA5T1qveps1zueeFcwU3CVBtcsaWa0Aou8RT8asLFW4OEi0iLNBK2FKuOUhMucfX+sJgkSBILLkREREREREREzaZfTbgEYykE1fEIRjkzpxR1chVcAOCWbX1odVgwMh/F48emDT0XKh9XbKkmPHYlYRKKJREQM1yqWXBRixUiLdIokmkZCXWgCFuKGcu8IOFSvKUY24kRERERERERETUnp9WMVrU7j5Epl1A8iZlQHACwqs2R91zedcEAACXlQvWFK4RUEx5bVkuxqFpwqWJLMbtarGi0hEssq0DElmLGkiQJokNYwYSL2lKM7cSIiIiIiIiIiJqXSLmM+Y2b43LOpxRzWh0WeB3WvJd714UDMEnAofFAVdqcUem4Yks1IVqKpWRgMqhUbZlwKS6WNXOGLcWMJ1IuhRIuInFkZcKFiIiIiIiIiKhp9alzXMYNLHCcVee3rM7TTkzocNmwY8ALAHjm5Kxh50Pl4woh1YTTatLSA1NBpSpczYSLIyvhIsuNM1xKJHLsFhPnhVSBxaS8RBZKuCTUhIuNCRciIiIiIiIioqbVV4WEiyi45Jvfku3K4Q4AwDMnfYadD5WPBReqCUmStJSLWMv2VrPgoqZDZADxVAMVXNREDtuJVYeWcCnwGBEtxSxMuBARERERERERNa3+KiRczvjUgktb6QWX58/4EE821tiEZsYVQqoZ96IWYrVoKQYA0UTjtBUTLcXYTqw6LKLgUiAFJVqK2VhwISIiIiIiIiJqWlVJuJRRcNnU7UaX24ZIIo2XRuYNOycqD1cIqWY8NrP2b5MEuLL+22gWs0lLL8QaqAIsWoqJlmhkLPEYSRVIuIiWYla2FCMiIiIiIiIialoi4WLkkPpyWopJkoQrh9sBcI5LPWHBhWrGk5VoabFbYKryTBKRcok2UMElxpZiVVVOwsXKhAsRERERERERUdPqb1ESLrPhhCEbuIOxJGbDCQDA6hIKLkD2HBcWXOoFVwipZrILLp4qthMTREqkkVqKiYSL3cKESzVkZrjk/yMqEi42JlyIiIiIiIiIiJpWq9OibYI2Yo7LOTXd0u60lrxWetmadpgl4NRsBCPzEd3PicrHggvVjMeeKRp4HTUouDRgwkWcq8PKp241iIRLqkDCJa4WXCxMuBARERERERERNS1JktCvznEZN2COy9k5pYhTSjsxwWO3YMdgKwDgmZM+3c+JyscVQqoZj21hS7FqE0WLRkq4iJZidrYUq4pMwqV4SzEbCy5ERERERERERE2tz8A5Lmd96vyWNkdZ17uKbcXqClcIqWayEy4tNUm4qC3FGinhorYUc7ClWFWUknARLcWsbClGRERERERERNTURMFlIqB/wuWM2lKsnIQLAFw53A4AeP7MnCGzZag8LLhQzWT3ImTCpTRsKVZd5SRcrEy4EBERERERERE1td4W4woumYRLeQWXDV1u9HhsiCXTeOncnO7nReXhCiHVTM0LLg2YcGFLseqymJTfcypdfIaLjQkXIiIiIiIiIqKm1teitPsyouByTk24rC4z4SJJEq5Q24o9zTkuNcdVW6qZBQWXWrQU0xIujVNwYUux6hJ1rWSBgkumpRhfTomIiIiIiIiImplIuIzrXHAJxpKYDScAAENlJlwAznGpJ1whpJrJnuHirckMF+XhL1IjjYAtxarLbC6ecGFLMSIiIiIiIiKilSG7pZhcYOZvuc6q6ZYOl3XBJvVS7VrdBrNJwhlfREvKUG1whZBqxmOr9QwXtaVYAyVc2FKsuiySOsOlhJZiVhNbihERERERERERNbMeteASS6YxH03qdtxK57cIHrsFOwa8AIAXz87pdVpUAa7aUs3UuqWYKFpEGynhIlqKWdlSrBos6lyWQgmXpEi4sAhGRERERERERNTU7BYTOlxWAMCEX7+2YiLhsqrM+S3ZLhhUCi77R/y6nBNVhiuEVDMLWoox4VKSmGgpxsX9qjBrCZf8jxEmXIiIiIiIiIiIVg4j5rgsN+ECADsHWgEA+0dZcKklrtpSzWQnXCrpTbhcjkZMuLClWFWJhEuyQE0uoaZfbJzhQkRERERERETU9DJzXKK6HfOMTznWchIu2wda1GNF4AvHdTkvKl/1V7mJVA6LCT0eG4KxFLo99qrfvihaxAqtptcZthSrrlISLgn18cOWYkREREREREREza/P6wAATORJuMxFEgCANqc15/dlWcaxqRCC8cwMmDO+MABg9TISLl6HFcOdLpycCePAqB/Xbuiq+FhUORZcqGYkScK/vvdCxJJpuGzVLyCwpRgVU8oMF7YUIyIiIiIiIiJaOTIJl6UFl1gyjfd+/0UAwI8+fEnOrj4/e3USdz5yJOexh9odyzq3nQNeFlxqTLeCy5e//GXcfffd+PKXv4x3vetdeS83OzuLK664oujx2trasGfPnrLPQ5Zl3H///fjv//5vHD58GIlEAn19fbj22mvxkY98BP39/WUfk4wjXqBqoZyWYrIs48CoH+P+GG48rxvmGi2us6VYdWUSLvkLLkm2FCMiIiIiIiIiWjG0GS7+pQWXU7NhTAWVdl4/OTCGD+xateD7aVnG9357FgDQ47HBmdXF5nXrO+G2LW+5fuegF//z8jj2j3COS63oUnB59NFH8YMf/KCkyx48eFCPm8wpnU7js5/9LB5++OEFXz99+jS+//3v4yc/+QnuuusuXH755YadAzWOUhIuM6E4HnllAve/PI7T6vCqVqcFl6/tqMo5LsaWYtVVUsJFTR2JyxIRERERERERUfPqK5BwOTkT1v79w70jePdFg7BmbdJ9+sQsTs6G4baZcW+eBMxy7BxoBQAcngggnkzDxk3bVbfse/Txxx/Hpz/9aaQLzDjI9sorrwAABgcH8eCDD+a9nCSVv3j593//91qx5Xd/93dx++23w+v14rnnnsNXv/pVjI+PY/fu3XjwwQfR19dX9vGpuWQSLksfu7Is429+eQwPHppYstg+Gajd0CnRUowJl+ooJeGSYMKFiIiIiIiIiGjFEAmXqWAMqbS8oBPOyZmQ9u/JYBw/f3USb9maWYe++3kl3XLbzn7diy0AMNTmQIfLitlwAocnAtg52Kr7bVBhFa8QptNpfOMb38Af/uEfIh4vfQH60KFDAIDt27fD7Xbn/Z/L5SrrfMbHx/Hv//7vAICPf/zj+PznP4/h4WF0dnbi5ptvxg9+8AO0trbC7/fjrrvuKuvY1JwcVrXgkljaUmw6FMf/vDyOVFrGtv4W/NkbNuL1GzoBAPPRRFXPM5toKcYZLtVhUYsohRIu8+ogNLedqSMiIiIiIiIiombX6bbBbJKQkpU1xGwn1ITLYKsyi+Xu589BlpV1pZdH/XhpxA+LScK7Lxo05NwkScKOAS8A4MAo24rVQkWrtr/5zW9w66234q677kI6ncbWrVtLvm52wUVPd999NxKJBFwuF37/939/yfeHhobwwQ9+EADw0EMPIRwOL7kMrSwOi9pSLEfCxR9NAgBaHRZ8770X4u07+rXqtfhetcmyzJZiVVYs4ZJMyzijtppb21FekZiIiIiIiIiIiBqP2SShx2MDsLStmGgptvuaYbhtZpyYCeOZkz4AwN0vnAMA3LylB90e4+Zas+BSWxUVXD760Y/iyJEjsFqt2L17N77+9a+XdL35+XmMjIwA0L/g8tRTTwEAdu3aBY/Hk/MyN9xwAwAgEongmWee0fX2qfEUSrgEY0pRJTva1+qwAqhdwSWRkiGW/ZlwqY5iM1zOzUWQTMtwWExaQY6IiIiIiIiIiJqbWAca90e1r8WTaZybUzbm7hjw4u07+gEA33/+LE7PhvHEsWkAwPt3DRl6bqKN2P4Rv5auoeqpaNVWkiTceOONuP/++/GpT30KJlNphxHpFkmSYLfbceedd+L666/Htm3bcPnll+MTn/gEnnjiibLPJx6P48SJEwBQMG2zadMmWCzKAvrBgwfLvh1qLoUSLsG4UoTJLrh4Hcq//TVqKSbaiQEsuFSL6MGZL+Eidi0Md7pgqmDuFBERERERERERNR5RcMlOuJyZiyAlA26bGV1uG9590SDMJgl7z83jSz8/ChnA1es6sK7Tbei5be7xwGaW4IskcHYuWvwKpKuKJvP89Kc/xfDwcNnXEwUXk8mE973vfUgmM0kBn8+HJ598Ek8++STe8Y534Etf+pJWHClmYmJCO9bgYP7+d2azGb29vRgZGdGSNrRyiYRLLJlGWpYXLJiHtIRLpnWX16k8HudrlHAR7cTMJkmbLULGspgKJ1xOzWYKLkREREREREREtDL0tigzWrILLmJj7rpOFyRJQm+LHTdt6cHDhyawX23v9cFdqww/N5vFhPP7WrBvxI/9I/NY3e40/DYpo6KCSyXFFiBTcEmlUhgeHsanPvUpXHzxxbBardi3bx++8Y1v4NVXX8V9990Hj8eDL37xiyUd1+fzaf9ubW0teFnRbszvL9zDjpvVFxK/j2b6vTiz5qAkUukFc1GC8UxLMfEzZ7cUq8XvIZ5S57dYTE11P9Qzi5ZwSef8nWcnXJZ7nzTjc4yoXvD5RWQcPr+IjMPnF5Fx+PwiMs5KeX71ezMJF/GznpwJAVi4TvSBS4bw8KEJAMD2/hZcOOStyu9mx4AX+0b8ODDqx1u39xl/g6SpqOBSqXg8DrfbjbVr1+Luu++G252JT9144424+uqr8aEPfQj79u3D3XffjXe+850477zzSjquYLcXnqPgcCjVx2g0f5yqo8MNMxMEOXV2ttT6FHSTnVpweV3ocNu0/5bVdFWn14GuLuVnXh1WijDBeEr7WjVNJZTzddosNbn9lahV3a1gyfM7P6v26dy5tlO3+6SZnmNE9YbPLyLj8PlFZBw+v4iMw+cXkXGa/fm1cbANADATSWprQqNBZQzB9tUd2te6ulrwpu19eOTlcXzmjZvR3e2tyvm9bksfvv/8ORyaCHIdscqqWnD51re+BQBIJpM524U5HA78xV/8BW677TbIsoz77rsPX/jCF4oeN3uGjFSkRCgGBRWaOzM7G2r6Kmy5JEl5oZyZCaCZZi3ZzBLiKRmjE/NIex3a1ydmlYq0VZYxPR0AAMgx5UVzLhzXvlZNE+pt2s1STW5/JYpGlGJuKMd9npZlHJsIAgA6rcu/T5r1OUZUD/j8IjIOn19ExuHzi8g4fH4RGWelPL+cUDrRnJsNa2tCh0fnAQA9DvOCdaIv3rABH790FVa1O6u2pjfsUTr1HJsM4vjZWbQ6rVW53WZXSvGqqgUX7UYLzGbZtm0bent7MTExgf3795d0PJcrMz8hFosVuGTm+8WSMM38grAcstxcvxuH1Yx4KolIPL3g5wrGlAH1HrtF+3qLXXnchuIpJJLpqs9RicSVF3KbxdRU90E9s6n3cSieWvI7H52PIpZMw2qWMNDq1O0+abbnGFE94fOLyDh8fhEZh88vIuPw+UVknGZ/fvV6lLVlXySBaCINswSc8UUAKC3Fsn92m9mEoTb91o5K0eq0Yk27E6d9ERwY9ePqdZ3Vu/EVri77Zg0MDABYOJulkJaWTGUpEChcJRTfb29vr/DsqJk4LMpTIJpMLfh6IJaZ4SK0ZP3br36/msQ5inMm423sVtoeHhoLaOk44dSM8kd0TbtLm/VCRERERERERETNz+uwaGt0k4EYzs1FkUzLcFpN6G0pvNG/WnYOKu3LXjpXeJY56asmK7eLFy4XEzNZnE5nScfr7+/XZrOMjo7mvVwqlcLEhDKkSBR1aGWzqy+MsWR6wdeDouBiM2tfM5skrejij1S/4CLO0WE1F7kk6WVLbwtsZgm+SAKn1V0Kwgl1ENraDleuqxIRERERERERUZOSJAl9XqWwMh6I4sRsGICyTmSqk1kVl65WAge/PDKJdDPHjepM1QouL730Eq6//nrs3LkTDzzwQN7LpVIpnDp1CgCwdu3ako5tMpmwYcMGAMDhw4fzXu7IkSNIpZSUwJYtW0o7cWpqongRTSwsuIRyJFwApXoNAPPRRBXObiFxjnYmXKrGZjFha7+yG2D/yPyC751S/5Cu62TBhYiIiIiIiIhopRFJlolADCfVjbn1tE507YZOeOxmjPljeOHMXK1PZ8Wo2srt4OAgRkdHEY1G8dRTT+W93GOPPYZQSHmAXnPNNSUf/9prrwUA7NmzB+FwOOdlHn30UQCAzWbDpZdeWvKxqXnlaykWjIsZLgvTJKLg4o+ypdhKcYGIX44sjF+enFF3LtTRH1IiIiIiIiIiIqqOvhal49K4P6atEw13umt5Sgs4rGa8cXMPAOCBg+M1PpuVo2ortz09PbjiiisAAI888ghefPHFJZeZmprCV77yFQBAX18f3vzmN5d8/FtuuQVmsxl+vx933XXXku+fPXsW3//+9wEAt912G7xebyU/BjUZe56ESzBPwqXVYQVQm4KLaCnGhEt1XTDYCmBhwkWWZZzQ/pCy4EJEREREREREtNJkJ1zqdZ3olm19AIDHj03DX4OOPStRVVdu77jjDtjtdqTTaXz84x/Hv/3bv+HUqVOYmprCgw8+iNtvvx0jIyOwWCz467/+a9jtCwcM3XPPPbjppptw00034cCBAwu+Nzw8jPe+970AgO9+97u48847cfz4cczOzuJnP/sZ3v/+98Pv96OtrQ2f+MQnqvYzU33Lm3CJqQkXW/21FOMMl+raMeCFBODcXBTTwRgAYDoURyiegkkCVreVNmuKiIiIiIiIiIiahyi4jPmjOF2nrefP7/VgQ5cb8ZSMX7w6VevTWREsxS+in82bN+Mb3/gGPvOZzyAYDOIrX/mKlmgRXC4X/uZv/gZXX331kuv7fD6cPHkSABCJRJZ8/3Of+xzOnj2LJ554Avfeey/uvffeJcf+zne+g4GBAR1/KmpkDsvShIssywjFRcKFLcVWOo/dgo3dbhydCmHfiB83ntet7VoYanPCxvuDiIiIiIiIiGjF6fUqBZcDo37EUzLsFhP6vY4an9VCkiThlm29+IcnTuCBg+N45wVcFzda1VcKr732WjzyyCP4yEc+go0bN8LpdMLpdGLdunX48Ic/jIcffhg333xzRce22+34zne+g7/927/FpZdeCq/XC6vVisHBQdx+++144IEHcOGFF+r8E1Ejc1hFwiVTcAknUkjLyr8XtxTzOuuhpRgTLtUm2ortU9uKnZqpz10LRERERERERERUHSLhElE3cq9pd8Jskmp5SjndvKUHFpOEwxNBHJsK1vp0mp4uCZehoSEcOXKk5Mv39vbijjvuwB133FHW7ezevRu7d+8ueBlJknDrrbfi1ltvLevYtDJpLcUSmZZiAbWYYjFJS+altGoJl1q2FGOiotouGGrFj/aNYt+IHwBwUo2Jru1gwYWIiIiIiIiIaCXqa1k4DqPe5rcI7S4brlnficeOTePBgxP4zHWeWp9SU+PKLa1oYh5KdsIlGFfnt9gtkKSFVenMDJdaJFzYUqxWLhj0AgCOTQURjCXrdhAaERERERERERFVh8Nq1jZnA8C6TncNz6awt27rAwA88soEEql0kUvTcnDllla0XAmXUCz3/BYA8DqW11Ls+HQIv3fvfrx4dq7s60bZUqxmuj12DLY6kJaBl8f8WksxFlyIiIiIiIiIiFau3qyUSz2vE122th3dHhvmo0n8+vhMrU+nqbHgQitazoRLTE242JZ23FtuS7FfHZnC3nPzePDgeNnXZUux2hIplyeOzcAXUe5/thQjIiIiIiIiIlq5+rwO7d/1XHCxmCS8+fxeAMADBydqfDbNjSu3tKJlEi7ZBRfjEi5iob6S67OlWG1dMNgKAPjZ4UkAwIDXDqeVaSMiIiIiIiIiopVKJFwsJglDbc4an01ht6htxZ49NYvJQKzGZ9O8uHJLK5pIi0STmZZiwbgouCxNuIgZLoFoEqm0XPbt+cJKwUUUdcrBlmK1JQouYbX93No63rVARERERERERETG61MLLms6nLCYpCKXrq3V7U5cOOhFWgYefoUpF6Ow4EIrmsOSv6WYu0DBRUZlRZM5kXCppODClmI1tabDiTanVfvv4Y76HYRGRERERERERETG2z6gtKDftbq9xmdSGpFyefDgOGS5/M3kVBxXbmlFs6vtuWI5Woq15Ci4WM0muNQ2UpW0BRMtxQJsKdZwJEnS5rgAwHBnfcdEiYiIiIiIiIjIWBcOteKnv385/uT162p9KiW5YVM3XFYzzs5FsW/EX+vTaUpcuaUVLVdLsYCY4WLL3bpLpFz80UTZtzenthQLLKOlmIMtxWpGtBUDgOFOJlyIiIiIiIiIiFa6LrcNJqm+24kJLpsZbzivGwDwwMHxGp9Nc2LBhVY0UbyIJZcmXHLNcAEyBZf5MlMqaVnGvFqkiSTSSKbSRa6xkGgpZmdLsZpZkHDp4AwXIiIiIiIiIiJqLLds6wUA/OrIFELx8jeFU2FcuaUVTUu4JDIJl1Bc+bfHnifhos7xKLelmD+SRDqrNWK5c1zYUqz2Nve24OYtPXjPRYNoceQuyBEREREREREREdWrHQNerGl3IppM41dHpmp9Ok2HK7e0oomES7SMhEtrhS3F5iILL1/OHJdUWkY8pVRr2FKsdswmCX/1ps34zHXra30qREREREREREREZZMkCW/d1gcAeODgRI3Ppvmw4EIrWq6ESzCmJlxs+rYU8y0uuJSRcMlueeZgSzEiIiIiIiIiIiKq0JvO74FZAg6M+nFqJlzr02kqXLmlFU2kReIpGSm131cm4ZKnpZijspZiiwsu5VxftBMDABtbihEREREREREREVGFujx2XDncAQB48NB4jc+muXDllla07LSISJEE1WFRbr1bioXjC/47WEbCRbQ8s1tMMElSWbdLRERERERERERElE20FXvo0ASSqXSRS1OpWHChFS07LRJNppCWZYTUlmIteQouXq3gUsWES0J50XMw3UJERERERERERETLdPW6DrQ7rZgNJ/DSyHytT6dpcPWWVjSTJMFuEXNc0gjHU5DV73nyFlyUlmLzkfIKLnOLLl/ODJeo2lLMzoILERERERERERERLZP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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-darkgrid') # 设置画图的风格\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "df3['收盘价(元)'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 58.同时绘制开盘价与收盘价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3[['开盘价(元)', '收盘价(元)']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 59.绘制涨跌幅的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fJ5zdS1tw/vnn5/HHH3/TM7Oef/75JEnPnj1bYlqwU3jhhRdy2WWXpV27drniiisaPdMXdne1v3MaNmxYg8uibvk7py5duggqOxlRhXrGjRuXXr16Zf369Rk7dmx+8Ytf5K9//Wv+8pe/5Ac/+EEuuuiiJMl73/vefOQjH2nl2cKOV3uWyn777ZfOnTvn2WefzVe+8pW8973vzbBhw/Kud70r55xzjhvU02Z95zvfydNPP51DDz00Z511VmtPB1pVt27dmgzsmzZtyu23354k6dOnTwYPHtySU4Md7qWXXkqSNz0Dq3///kk232uiqbPjYXfT3P2DXn755TzwwANJfHCRtmPTpk358pe/nNdffz2nn356jjjiiNaeErSK2t851Z7RPnfu3IwdOzaHHXZYhg0blve///255JJLsmzZstacJo3YJS//xY6z//7758c//nGmTZuWRx55JJMmTaq3f4899sjZZ5+ds846q+5SYbA7W7FiRZLN18L/+c9/nilTptT7BcBrr72W2bNn51e/+lWmTJnihmG0Kb/5zW9y8803p0uXLrniiiucrQXNuPHGG/Pss88mSf7t3/7N/0ex21m1alWSNz8Lq0ePHkk237h+zZo16dOnzw6fG+ysqqurM3Xq1LqfL8aMGdPKM4KW8f3vfz+PPfZY/sf/+B9N3m8I2oLayz726tUrX/va1/KTn/yk3v4VK1bkpz/9ae67775cf/31Oeyww1pjmjTCT3M0sGbNmnTt2rXR08reeOONPPnkk/UuXwG7s9deey1JsnTp0kyZMiV77713vv3tb+c3v/lNHnvssVx33XUZNGhQqqurM23atDz44IOtPGNoGWvXrs1XvvKVVFdXZ9KkSTnwwANbe0qw07rvvvty9dVXJ0kGDRqUL3zhC607IdgB1q9fn2Tz5Sma07lz5wbHQFt12WWX1f38cNxxx+Woo45q5RnBjvfkk0/me9/7Xjp06JArrrii3vsCtDW1v3OaNWtWfvKTn+Td7353br311jzxxBN56KGHcvHFF6dr165ZvXp1vvjFLzpjZSciqlDPPffck9NOOy1z587Ne97znrqF/Oijj+baa6/N4MGDM3/+/IwZMya//e1vW3u6sMO98cYbSTZ/+nLPPffMT3/603z0ox9Njx490rVr14waNSo/+clPsvfee6empiZXXnllK88YWsa0adOydOnSvPe9782pp57a2tOBndY999yTL33pS6murk737t1zzTXXNHsZGNhV1Z6t+GbX+66pqanbdsYWbVVNTU0uvfTSzJgxI0ly0EEHZdq0aa08K9jx1q1bly9/+cupqqrKF7/4xQwbNqy1pwStat26dUk2n5Fy5JFH5oc//GHe/e53p3PnzunTp09OOeWU/K//9b/Srl27rFq1Kt///vdbecbU8n+xbURNTU1ee+21Jv9s3Lgxf/vb3+pOPR45cmR+8IMf1C3kPffcM8cee2xuu+22HHjggXnttdfy1a9+NZs2bWrtlwbbrGRd7LHHHnXjzzzzzOy7774Nvk7fvn0zduzYJMmzzz6bxYsXt9hrgO2tZF3cd999+fnPf57u3bvnm9/8phvmsVsrWRNNueWWWzJp0qRUVVWlW7du+f73v58hQ4a04Oyh5XTt2jXJ33850JQNGzbUbXfq1GmHzgl2Rhs2bMiXvvSl/OhHP0qSvP3tb8/NN9/sJt20CVdeeWX+9Kc/Zfjw4Rk/fnxrTwda3ZZn+E6ePDkdO3ZsMOaII47IyJEjkyS//OUvW2xuNM89VdqIpUuXZvTo0U3u/+Y3v5l169Zl7dq1SZILLrig0U+O9ejRI5MmTco555yTJUuW5OGHH8773ve+HTZv2JFK1sWWP9y85z3vaXLsu9/97rrtxYsX5+1vf/v2mSS0sDdbFxMnTszNN9+cJJkyZUrdDYdhd1XyXnHiiSfWe2zTpk35xje+UXdN5F69euWGG27IoYceukPnCq2p9gysNWvWNDvu1VdfTbL5LJWePXvu8HnBzqSysjLnnHNOfve73yVJhg4dmhtvvNG9hWgTHnzwwdx6663p3LlzrrjiinTo4FeS0K1bt7z++uvp0aNHhg4d2uS4I444InPnzs2KFSuyevXq9OrVq+UmSaP8C0ad2vuk9OzZs9lfCB9xxBF123/6059EFXZrAwcOrNtu7lqvtTddTd78E5qwK9t7772zevXqJJs/STN58uQmx/7617+u+1R+Y794ht3Ra6+9lokTJ2bevHlJkv333z833HBDBg8e3LoTgx1s8ODB+fWvf52XX3652XG1+/v16+fyX7QpS5Ysybhx4/L8888nST7wgQ/kO9/5jjNUaDPuvvvuJJvvp/XRj3602bHXXnttrr322iTJnDlz6v1cDruTgQMHZsWKFW96b6Etf+fknnQ7B1GljRg4cGAWLVrU7Jivfe1rSZKqqqpmx235w8+bjYWdWcm6uPfee+u2X3jhhSaD4yuvvFK33dglwmBX8Wbr4s4772zB2UDrK3mvqLVmzZqcccYZ+cMf/pAk+ad/+qdcd9116du3746cIuwUaiP60qVL8+qrr2bPPfdsdNzChQuTJAcffHCLzQ1a2zPPPJPPfe5zWbVqVZLkpJNOyiWXXOKT+gBt3CGHHJLHHnsslZWVWbt2bZP3Xqz9nVP79u2z1157teQUaYJ3cOoceOCBSZLXX389TzzxRJOXqPjNb37T4BjYXf3Lv/xLOnXqlA0bNuSee+7JBz/4wUbH/fd//3eSzdcGP+SQQ1pwhtCyPvaxj+XDH/5ws2PGjh2bxx57LIcffnhuuOGGJM2f6QW7g/Xr1+fMM8+sCypHH310rr766nrXSYbd2YgRI5Ik1dXVmTdvXj72sY81GPPCCy/k2WefTbL5U/rQFjz//PP5/Oc/XxdU/v3f/z1nn312K88KWt7Xv/71TJ06tcn91dXVdZfVPuuss3LWWWcl+fs9u2B39MEPfjA/+clPUl1dnfvuuy+f+tSnGh1X+zun4cOHp3379i05RZrgfGvqHHvssXU3i/zmN79Z7yaStdauXZv/+I//SJLstddeee9739uic4SW1rNnz7pfIM+aNSsPP/xwgzF/+ctfMmPGjCSb11FTn8yE3UGHDh3SrVu3Zv/U3ri+ffv2dY/5JCa7uyuvvDKPPfZYkuTDH/5wrr32WkGFNmX//ffP4YcfniSZPn16g3ur1NTU5PLLL09NTU169+6dj3/8460xTWhRGzZsyPnnn5+VK1cmSS688EJBhTarU6dOzf4Msccee9SN7dixY4OfLWB39L73vS8DBgxIklx99dVZsWJFgzH33Xdf3QfcP/GJT7To/GiaqEKd/fbbL1/84heTJL/73e9y0kkn5Ze//GWWL1+eV155Jffdd19OOumkuk+XffWrX/XLAtqECy64IL179051dXXGjRuX66+/Ps8//3xWrlyZWbNm5ZRTTsmaNWvSo0ePfPnLX27t6QLQwhYtWpRbb701SfK2t70tF198cdavX5/XXnutyT+bNm1q5VnD9nfhhRemXbt2WbJkScaMGZMFCxaksrIyCxcuzDnnnJPZs2cnSSZMmOCTx7QJP/vZz+ouefeRj3wkn/70p5t9b3jttddaecYAtKQOHTrk61//etq1a5cVK1bk05/+dH7+859n2bJlWbp0aa6//vp86UtfSpIceuih+fSnP93KM6ZWRU1NTU1rT4Kdy1VXXZUbbrghTf3V6NixY7761a9mzJgxLTwzaD3PPvtszjrrrLz00kuN7u/Vq1euueaavOc972nhmcHO5+STT87vfve7HHnkkXVnccHubMqUKbntttu26pgf/ehH3jPYLd15552ZOnVqNm7c2Oj+M844I5MnT27hWUHr+NCHPlR3Y/pSpffxgt3Nxo0bM3To0CTJueeemwkTJrTyjKDl3H333fnqV7+adevWNbr/kEMOyXXXXZd+/fq18Mxoimtx0MCkSZPyoQ99KD/5yU/y61//OsuXL0/79u3Tr1+/HHXUUTn11FMzePDg1p4mtKiDDjood999d3784x/nl7/8Zf785z+nuro6/fv3z6hRo/LZz37WDeoB2qjHH3+8tacAO40TTzwxQ4cOzU033ZRHHnkkK1euTNeuXTNs2LCMGTMmxxxzTGtPEVrEqlWrtjqoANA2HXfccTnssMPywx/+MPPnz8/LL7+czp07Z9CgQTn++OPzqU99qt4l8mh9zlQBAAAAAAAo4J4qAAAAAAAABUQVAAAAAACAAqIKAAAAAABAAVEFAAAAAACggKgCAAAAAABQQFQBAAAAAAAoIKoAAAAAAAAUEFUAAAAAAAAKiCoAAAAAAAAFRBUAAAAAAIACogoAAAAAAEABUQUAAAAAAKCAqAIAAAAAAFBAVAEAAAAAACjw/wPrqRhcuz1tkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3['涨跌幅(%)'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 60.让直方图更细致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df3['涨跌幅(%)'].plot(kind='hist', bins=20)\n",
    "df3['涨跌幅(%)'].hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 61.以data的列名创建一个dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [代码, 简称, 日期, 前收盘价(元), 开盘价(元), 最高价(元), 最低价(元), 收盘价(元), 成交量(股), 成交金额(元), 涨跌(元), 涨跌幅(%), 均价(元), 换手率(%), A股流通市值(元), 总市值(元), A股流通股本(股), 市盈率]\n",
       "Index: []"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(columns=df3.columns)\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 62.打印所有换手率不是数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
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       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(df3)):\n",
    "    if type(df3['换手率(%)'][i]) != float:\n",
    "        temp = temp.append(df3.iloc[i])  # DataFrame.append()返回的是一个DataFrame的对象，和列表的append区别 \n",
    "    \n",
    "temp\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 63.打印所有换手率为--的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 方法一\n",
    "# df3[df3['换手率(%)'] == '--']\n",
    "\n",
    "# 方法二\n",
    "# df3[df3['换手率(%)'].isin('--')]  # 错误\n",
    "df3[df3['换手率(%)'].isin(['--'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 64.重置data的行号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>11350479</td>\n",
       "      <td>199502702</td>\n",
       "      <td>-0.5211</td>\n",
       "      <td>-3.2597</td>\n",
       "      <td>17.5766</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.6675</td>\n",
       "      <td>15.7912</td>\n",
       "      <td>14.9345</td>\n",
       "      <td>15.4467</td>\n",
       "      <td>71918296</td>\n",
       "      <td>1262105060</td>\n",
       "      <td>-0.0177</td>\n",
       "      <td>-0.1142</td>\n",
       "      <td>17.5492</td>\n",
       "      <td>0.3855</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index         代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)  \\\n",
       "0      0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997   \n",
       "1      1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672   \n",
       "2      2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234   \n",
       "3      3  600000.SH  浦发银行 2016-01-07  15.9855  15.7205  15.8088  15.3672   \n",
       "4      4  600000.SH  浦发银行 2016-01-08  15.4644  15.6675  15.7912  14.9345   \n",
       "\n",
       "    收盘价(元)    成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)  \\\n",
       "0  15.7205  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264   \n",
       "1  15.8618  58054793  1034181474  0.1413  0.8989  17.8139  0.3112   \n",
       "2  15.9855  46772653   838667398  0.1236  0.7795  17.9307  0.2507   \n",
       "3  15.4644  11350479   199502702 -0.5211 -3.2597  17.5766  0.0608   \n",
       "4  15.4467  71918296  1262105060 -0.0177 -0.1142  17.5492  0.3855   \n",
       "\n",
       "      A股流通市值(元)        总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  3.350163e+11  1.865347e+10  6.6204  \n",
       "2  3.376278e+11  3.376278e+11  1.865347e+10  6.6720  \n",
       "3  3.266223e+11  3.266223e+11  1.865347e+10  6.4545  \n",
       "4  3.262492e+11  3.262492e+11  1.865347e+10  6.4471  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df3.reset_index()\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 65.删除所有换手率为非数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3.drop(labels=temp)  # 错误\n",
    "\n",
    "k = []\n",
    "for i in range(len(df3)):\n",
    "    if type(df3['换手率(%)'][i]) != float:\n",
    "        k.append(i)\n",
    "        \n",
    "df3.drop(labels=k, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 66.绘制换手率的密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3['换手率(%)'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 67.计算前一天与后一天收盘价的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>11350479</td>\n",
       "      <td>199502702</td>\n",
       "      <td>-0.5211</td>\n",
       "      <td>-3.2597</td>\n",
       "      <td>17.5766</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.6675</td>\n",
       "      <td>15.7912</td>\n",
       "      <td>14.9345</td>\n",
       "      <td>15.4467</td>\n",
       "      <td>71918296</td>\n",
       "      <td>1262105060</td>\n",
       "      <td>-0.0177</td>\n",
       "      <td>-0.1142</td>\n",
       "      <td>17.5492</td>\n",
       "      <td>0.3855</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234  15.9855   \n",
       "3  600000.SH  浦发银行 2016-01-07  15.9855  15.7205  15.8088  15.3672  15.4644   \n",
       "4  600000.SH  浦发银行 2016-01-08  15.4644  15.6675  15.7912  14.9345  15.4467   \n",
       "\n",
       "     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n",
       "1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n",
       "2  46772653   838667398  0.1236  0.7795  17.9307  0.2507  3.376278e+11   \n",
       "3  11350479   199502702 -0.5211 -3.2597  17.5766  0.0608  3.266223e+11   \n",
       "4  71918296  1262105060 -0.0177 -0.1142  17.5492  0.3855  3.262492e+11   \n",
       "\n",
       "         总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  1.865347e+10  6.6204  \n",
       "2  3.376278e+11  1.865347e+10  6.6720  \n",
       "3  3.266223e+11  1.865347e+10  6.4545  \n",
       "4  3.262492e+11  1.865347e+10  6.4471  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 68.计算前一天与后一天收盘价变化率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 69.设置日期为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>11350479</td>\n",
       "      <td>199502702</td>\n",
       "      <td>-0.5211</td>\n",
       "      <td>-3.2597</td>\n",
       "      <td>17.5766</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>3.266223e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.6675</td>\n",
       "      <td>15.7912</td>\n",
       "      <td>14.9345</td>\n",
       "      <td>15.4467</td>\n",
       "      <td>71918296</td>\n",
       "      <td>1262105060</td>\n",
       "      <td>-0.0177</td>\n",
       "      <td>-0.1142</td>\n",
       "      <td>17.5492</td>\n",
       "      <td>0.3855</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>3.262492e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.1600</td>\n",
       "      <td>15.1600</td>\n",
       "      <td>15.1600</td>\n",
       "      <td>15.0500</td>\n",
       "      <td>15.0800</td>\n",
       "      <td>14247943</td>\n",
       "      <td>215130847</td>\n",
       "      <td>-0.0800</td>\n",
       "      <td>-0.5277</td>\n",
       "      <td>15.0991</td>\n",
       "      <td>0.0659</td>\n",
       "      <td>3.260037e+11</td>\n",
       "      <td>3.260037e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.0800</td>\n",
       "      <td>15.0700</td>\n",
       "      <td>15.0700</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>19477788</td>\n",
       "      <td>291839737</td>\n",
       "      <td>-0.1000</td>\n",
       "      <td>-0.6631</td>\n",
       "      <td>14.9832</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>3.238418e+11</td>\n",
       "      <td>3.238418e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.0988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>14.9500</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>14.5200</td>\n",
       "      <td>14.9200</td>\n",
       "      <td>40194577</td>\n",
       "      <td>592160198</td>\n",
       "      <td>-0.0600</td>\n",
       "      <td>-0.4005</td>\n",
       "      <td>14.7323</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>3.225447e+11</td>\n",
       "      <td>3.225447e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.0744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>14.9200</td>\n",
       "      <td>14.7800</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>14.5100</td>\n",
       "      <td>14.8600</td>\n",
       "      <td>43568576</td>\n",
       "      <td>638781010</td>\n",
       "      <td>-0.0600</td>\n",
       "      <td>-0.4021</td>\n",
       "      <td>14.6615</td>\n",
       "      <td>0.2015</td>\n",
       "      <td>3.212476e+11</td>\n",
       "      <td>3.212476e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>14.8600</td>\n",
       "      <td>14.6900</td>\n",
       "      <td>14.8400</td>\n",
       "      <td>14.6600</td>\n",
       "      <td>14.7600</td>\n",
       "      <td>19225492</td>\n",
       "      <td>283864640</td>\n",
       "      <td>-0.1000</td>\n",
       "      <td>-0.6729</td>\n",
       "      <td>14.765</td>\n",
       "      <td>0.0889</td>\n",
       "      <td>3.190858e+11</td>\n",
       "      <td>3.190858e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.0093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>327 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   代码    简称  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "日期                                                                         \n",
       "2016-01-04  600000.SH  浦发银行  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "2016-01-05  600000.SH  浦发银行  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "2016-01-06  600000.SH  浦发银行  15.8618  15.8088  16.0208  15.6234  15.9855   \n",
       "2016-01-07  600000.SH  浦发银行  15.9855  15.7205  15.8088  15.3672  15.4644   \n",
       "2016-01-08  600000.SH  浦发银行  15.4644  15.6675  15.7912  14.9345  15.4467   \n",
       "...               ...   ...      ...      ...      ...      ...      ...   \n",
       "2017-05-03  600000.SH  浦发银行  15.1600  15.1600  15.1600  15.0500  15.0800   \n",
       "2017-05-04  600000.SH  浦发银行  15.0800  15.0700  15.0700  14.9000  14.9800   \n",
       "2017-05-05  600000.SH  浦发银行  14.9800  14.9500  14.9800  14.5200  14.9200   \n",
       "2017-05-08  600000.SH  浦发银行  14.9200  14.7800  14.9000  14.5100  14.8600   \n",
       "2017-05-09  600000.SH  浦发银行  14.8600  14.6900  14.8400  14.6600  14.7600   \n",
       "\n",
       "              成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)  \\\n",
       "日期                                                                  \n",
       "2016-01-04  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264   \n",
       "2016-01-05  58054793  1034181474  0.1413  0.8989  17.8139  0.3112   \n",
       "2016-01-06  46772653   838667398  0.1236  0.7795  17.9307  0.2507   \n",
       "2016-01-07  11350479   199502702 -0.5211 -3.2597  17.5766  0.0608   \n",
       "2016-01-08  71918296  1262105060 -0.0177 -0.1142  17.5492  0.3855   \n",
       "...              ...         ...     ...     ...      ...     ...   \n",
       "2017-05-03  14247943   215130847 -0.0800 -0.5277  15.0991  0.0659   \n",
       "2017-05-04  19477788   291839737 -0.1000 -0.6631  14.9832  0.0901   \n",
       "2017-05-05  40194577   592160198 -0.0600 -0.4005  14.7323  0.1859   \n",
       "2017-05-08  43568576   638781010 -0.0600 -0.4021  14.6615  0.2015   \n",
       "2017-05-09  19225492   283864640 -0.1000 -0.6729   14.765  0.0889   \n",
       "\n",
       "               A股流通市值(元)        总市值(元)     A股流通股本(股)     市盈率  \n",
       "日期                                                            \n",
       "2016-01-04  3.320318e+11  3.320318e+11  1.865347e+10  6.5614  \n",
       "2016-01-05  3.350163e+11  3.350163e+11  1.865347e+10  6.6204  \n",
       "2016-01-06  3.376278e+11  3.376278e+11  1.865347e+10  6.6720  \n",
       "2016-01-07  3.266223e+11  3.266223e+11  1.865347e+10  6.4545  \n",
       "2016-01-08  3.262492e+11  3.262492e+11  1.865347e+10  6.4471  \n",
       "...                  ...           ...           ...     ...  \n",
       "2017-05-03  3.260037e+11  3.260037e+11  2.161828e+10  6.1395  \n",
       "2017-05-04  3.238418e+11  3.238418e+11  2.161828e+10  6.0988  \n",
       "2017-05-05  3.225447e+11  3.225447e+11  2.161828e+10  6.0744  \n",
       "2017-05-08  3.212476e+11  3.212476e+11  2.161828e+10  6.0500  \n",
       "2017-05-09  3.190858e+11  3.190858e+11  2.161828e+10  6.0093  \n",
       "\n",
       "[327 rows x 17 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.set_index('日期')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 70.以5个数据作为一个数据滑动窗口，在这个5个数据上取均值(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 71.以5个数据作为一个数据滑动窗口，计算这五个数据总和(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72.将收盘价5日均线、20日均线与原始数据绘制在同一个图上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 73.按周为采样规则，取一周收盘价最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 74.绘制重采样数据与原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 75.将数据往后移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 76.将数据向前移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 77.使用expending函数计算开盘价的移动窗口均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 78.绘制上一题的移动均值与原始数据折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 79.计算布林指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80.计算布林线并绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四期 当Pandas遇上NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 81.导入并查看pandas与numpy版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 82.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 83.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 84.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 85.将df1，df2，df3按照行合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 86.将df1，df2，df3按照列合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 87.查看df所有数据的最小值、25%分位数、中位数、75%分位数、最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 88.修改列名为col1,col2,col3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 89.提取第一列中不在第二列出现的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 90.提取第一列和第二列出现频率最高的三个数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 91.计算第一列数字前一个与后一个的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 92.将col1,col2,clo3三列顺序颠倒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 93.提取第一列位置在1,10,15的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 94.查找第一列的局部最大值位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#备注 即比它前一个与后一个数字的都大的数字\n",
    "# tem = np.diff(np.sign(np.diff(df['col1'])))\n",
    "# np.where(tem == -2)[0] + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 95. 按行计算df的每一行均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df[['col1','col2','col3']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 96.对第二列计算移动平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 97.将数据按照第三列值的大小升序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 98.将第一列大于50的数字修改为'高'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 99.计算第二列与第三列之间的欧式距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第五期 一些补充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 101.从CSV文件中读取指定数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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